Intel® oneAPI Math Kernel Library
for Windows*
Developer Guide

Intel® oneAPI Math Kernel Library- Windows*

Notices and Disclaimers
2023.0Intel® oneAPI Math Kernel Libraryfor Windows* Developer Guide

Contents
Notices and Disclaimers....................................................................... 6
Getting Help and Support..................................................................... 7
Introducing the Intel® oneAPI Math Kernel Library.............................. 8
Notational Conventions...................................................................... 10
Related Information .......................................................................... 11
Chapter 1: Getting Started

Shared Library Versioning......................................................................... 12
CMake Config for oneMKL ......................................................................... 12
Checking Your Installation ........................................................................ 13
Setting Environment Variables................................................................... 14
Compiler Support .................................................................................... 14
Using Code Examples............................................................................... 15
Before You Begin Using the Intel® oneAPI Math Kernel Library ....................... 15

Chapter 2: Structure of the Intel® oneAPI Math Kernel LibraryArchitecture Support................................................................................ 18
High-level Directory Structure ................................................................... 18
Layered Model Concept ............................................................................ 19

Chapter 3: Linking Your Application with the Intel® oneAPI Math
Kernel Library

Linking Quick Start .................................................................................. 21
Using the /Qmkl Compiler Option ...................................................... 21
Automatically Linking a Project in the Visual Studio* Integrated
Development Environment with Intel® oneAPI Math Kernel Library .... 22
Automatically Linking Your Microsoft Visual C/C++* Project with
oneMKL ............................................................................. 22
Automatically Linking Your Intel® Visual Fortran Project with
oneMKL ............................................................................. 22
Using the Single Dynamic Library ...................................................... 23
Selecting Libraries to Link with.......................................................... 23
Using the Link-line Advisor ............................................................... 24
Using the Command-line Link Tool ..................................................... 24
Linking Examples .................................................................................... 26
Linking on IA-32 Architecture Systems ............................................... 26
Linking on Intel(R) 64 Architecture Systems ....................................... 26
Linking in Detail ...................................................................................... 27
Dynamically Selecting the Interface and Threading Layer...................... 28
Linking with Interface Libraries ......................................................... 29
Using the ILP64 Interface vs. LP64 Interface .............................. 29
Linking with Fortran 95 Interface Libraries.................................. 31
Linking with Threading Libraries ........................................................ 31
Linking with Computational Libraries.................................................. 33
Linking with Compiler Run-time Libraries ............................................ 34
Linking with System Libraries............................................................ 34
Building Custom Dynamic-link Libraries ...................................................... 34
Using the Custom Dynamic-link Library Builder in the Command-line
Mode ......................................................................................... 35

2

Contents

Composing a List of Functions .......................................................... 37
Specifying Function Names ............................................................... 38
Building a Custom Dynamic-link Library in the Visual Studio*
Development System................................................................... 38
Distributing Your Custom Dynamic-link Library .................................... 39
Building a Universal Windows Driver .......................................................... 39

Chapter 4: Managing Performance and Memory

Improving Performance with Threading ...................................................... 43
OpenMP* Threaded Functions and Problems ....................................... 43
Functions Threaded with Intel® Threading Building Blocks ..................... 45
Avoiding Conflicts in the Execution Environment .................................. 46
Techniques to Set the Number of Threads........................................... 47
Setting the Number of Threads Using an OpenMP* Environment
Variable ..................................................................................... 47
Changing the Number of OpenMP* Threads at Run Time....................... 48
Using Additional Threading Control .................................................... 50
oneMKL-specific Environment Variables for OpenMP Threading
Control .............................................................................. 50
MKL_DYNAMIC ....................................................................... 51
MKL_DOMAIN_NUM_THREADS.................................................. 52
MKL_NUM_STRIPES .................................................................... 54
Setting the Environment Variables for Threading Control .............. 54
Calling oneMKL Functions from Multi-threaded Applications ................... 55
Using Intel® Hyper-Threading Technology ........................................... 56
Managing Multi-core Performance ...................................................... 57
Managing Performance with Heterogeneous Cores ............................... 58
Improving Performance for Small Size Problems ......................................... 58
Using MKL_DIRECT_CALL in C Applications ......................................... 59
Using MKL_DIRECT_CALL in Fortran Applications ................................. 59
Limitations of the Direct Call ............................................................ 60
Other Tips and Techniques to Improve Performance ..................................... 60
Coding Techniques .......................................................................... 61
Improving oneMKL Performance on Specific Processors ........................ 62
Operating on Denormals .................................................................. 62
Using Memory Functions........................................................................... 62
Memory Leaks in Intel® oneAPI Math Kernel Library ............................. 62
Redefining Memory Functions ........................................................... 62

Chapter 5: Language-specific Usage Options

Using Language-Specific Interfaces with Intel® oneAPI Math Kernel Library...... 64
Interface Libraries and Modules......................................................... 64
Fortran 95 Interfaces to LAPACK and BLAS ......................................... 65
Compiler-dependent Functions and Fortran 90 Modules ........................ 66
Mixed-language Programming with the Intel Math Kernel Library ................... 66
Calling LAPACK, BLAS, and CBLAS Routines from C/C++ Language
Environments ............................................................................. 67
Using Complex Types in C/C++......................................................... 68
Calling BLAS Functions that Return the Complex Values in C/C++ Code.. 69

Chapter 6: Obtaining Numerically Reproducible Results

Getting Started with Conditional Numerical Reproducibility ........................... 73
Specifying Code Branches......................................................................... 74
Reproducibility Conditions......................................................................... 76
Setting the Environment Variable for Conditional Numerical Reproducibility ..... 773

Intel® oneAPI Math Kernel Libraryfor Windows* Developer Guide

Code Examples ....................................................................................... 77

Chapter 7: Coding Tips

Example of Data Alignment....................................................................... 80
Using Predefined Preprocessor Symbols for Intel® MKL Version-DependentCompilation ........................................................................................ 81

Chapter 8: Managing Output

Using oneMKL Verbose Mode..................................................................... 83
Version Information Line .................................................................. 84
Call Description Line ........................................................................ 86

Chapter 9: Working with the Intel® oneAPI Math Kernel Library
Cluster Software

Message-Passing Interface Support............................................................ 90
Linking with oneMKL Cluster Software ........................................................ 91
Determining the Number of OpenMP* Threads ............................................ 92
Using DLLs ............................................................................................. 92
Setting Environment Variables on a Cluster ................................................. 93
Interaction with the Message-passing Interface ........................................... 93
Using a Custom Message-Passing Interface ................................................. 94
Examples of Linking for Clusters ................................................................ 95
Examples for Linking a C Application.................................................. 95
Examples for Linking a Fortran Application.......................................... 96

Chapter 10: Managing Behavior of the Intel® oneAPI Math Kernel
Library with Environment Variables

Managing Behavior of Function Domains with Environment Variables ............. 98
Setting the Default Mode of Vector Math with an Environment Variable ... 98
Managing Performance of the Cluster Fourier Transform Functions ......... 99
Managing Invalid Input Checking in LAPACKE Functions...................... 100
Instruction Set Specific Dispatching on Intel® Architectures ......................... 101

Chapter 11: Programming with Intel® oneAPI Math Kernel Libraryin the Visual Studio* Integrated Development Environment

Configuring Your Integrated Development Environment to link with Intel®
oneAPI Math Kernel Library ................................................................ 103
Configuring the Microsoft Visual C/C++* Development System to Link
with Intel® oneAPI Math Kernel Library ......................................... 103
Configuring Intel(R) Visual Fortran to Link with Intel® oneAPI Math
Kernel Library ........................................................................... 104
Getting Assistance for Programming in the Microsoft Visual Studio* IDE ....... 104
Using Context-Sensitive Help .......................................................... 104
Using the IntelliSense* Capability.................................................... 104

Chapter 12: Intel® oneAPI Math Kernel Library Benchmarks
Intel Optimized LINPACK Benchmark for Windows* .................................... 107
Contents ...................................................................................... 107
Running the Software .................................................................... 108
Known Limitations ......................................................................... 109
Intel® Distribution for LINPACK* Benchmark.............................................. 109
Overview ..................................................................................... 109
Contents ...................................................................................... 110

4

Contents

Building the Intel® Distribution for LINPACK* Benchmark for a
Customized MPI Implementation ................................................. 110
Building the Netlib HPL from Source Code......................................... 111
Configuring Parameters.................................................................. 111
Ease-of-use Command-line Parameters ............................................ 112
Running the Intel® Distribution for LINPACK* Benchmark.................... 112
Heterogeneous Support in the Intel® Distribution for LINPACK*
Benchmark............................................................................... 113
Environment Variables ................................................................... 115
Improving Performance of Your Cluster ............................................ 116

Appendix A: Appendix A: Intel® oneAPI Math Kernel Library
Language Interfaces Support

Language Interfaces Support, by Function Domain..................................... 117
Include Files ......................................................................................... 118

Appendix B: Support for Third-Party Interfaces

FFTW Interface Support ......................................................................... 120

Appendix C: Appendix C: Directory Structure In Detail

Detailed Structure of the IA-32 Architecture Directories .............................. 121
Static Libraries in the lib\ia32_win Directory ..................................... 121
Dynamic Libraries in the lib\ia32_win Directory ................................. 122
Contents of the redist\ia32 Directory ............................................. 122
Detailed Structure of the Intel(R) 64 Architecture Directories ...................... 125
Static Libraries in the lib\intel64_win Directory.................................. 125
Dynamic Libraries in the lib\intel64_win Directory.............................. 127
Contents of the redist\intel64 Directory ........................................ 128

5

Intel® oneAPI Math Kernel Libraryfor Windows* Developer Guide

Notices and DisclaimersIntel technologies may require enabled hardware, software or service activation.
No product or component can be absolutely secure.
Your costs and results may vary.
© Intel Corporation. Intel, the Intel logo, and other Intel marks are trademarks of Intel Corporation or its

subsidiaries. Other names and brands may be claimed as the property of others.
Product and Performance Information
Performance varies by use, configuration and other factors. Learn more at www.Intel.com/
PerformanceIndex.
Notice revision #20201201
No license (express or implied, by estoppel or otherwise) to any intellectual property rights is granted by thisdocument.
The products described may contain design defects or errors known as errata which may cause the productto deviate from published specifications. Current characterized errata are available on request.
Intel disclaims all express and implied warranties, including without limitation, the implied warranties ofmerchantability, fitness for a particular purpose, and non-infringement, as well as any warranty arising fromcourse of performance, course of dealing, or usage in trade.
Microsoft, Windows, and the Windows logo are trademarks, or registered trademarks of Microsoft Corporationin the United States and/or other countries.
Java is a registered trademark of Oracle and/or its affiliates.

6

Getting Help and Support

Getting Help and Support
Intel provides a support web site that contains a rich repository of self help information, including getting
started tips, known product issues, product errata, license information, user forums, and more. Visit theIntel® oneAPI Math Kernel Library support website athttp://www.intel.com/software/products/support/.
You can get context-sensitive help when editing your code in the Microsoft Visual Studio* integrated
development environment (IDE). See Getting Assistance for Programming in the Microsoft Visual Studio* IDE
for details.

7Intel® oneAPI Math Kernel Libraryfor Windows* Developer Guide

Introducing the Intel® oneAPI Math
Kernel LibraryIntel® oneAPI Math Kernel Library (oneMKL) is a computing math library of highly optimized, extensively
threaded routines for applications that require maximum performance. The library provides Fortran and C
programming language interfaces. oneMKL C language interfaces can be called from applications written ineither C or C++, as well as in any other language that can reference a C interface.
oneMKL provides comprehensive functionality support in these major areas of computation:
•
•
•

•

•
•

•
•

BLAS (level 1, 2, and 3) and LAPACK linear algebra routines, offering vector, vector-matrix, and matrixmatrix operations.
ScaLAPACK distributed processing linear algebra routines, as well as the Basic Linear Algebra
Communications Subprograms (BLACS) and the Parallel Basic Linear Algebra Subprograms (PBLAS).
oneMKL PARDISO (a direct sparse solver based on Parallel Direct Sparse Solver PARDISO*), an iterative
sparse solver, and supporting sparse BLAS (level 1, 2, and 3) routines for solving sparse systems ofequations, as well as a distributed version of oneMKL PARDISO solver provided for use on clusters.
Fast Fourier transform (FFT) functions in one, two, or three dimensions with support for mixed radices(not limited to sizes that are powers of 2), as well as distributed versions of these functions provided foruse on clusters.
Vector Mathematics (VM) routines for optimized mathematical operations on vectors.
Vector Statistics (VS) routines, which offer high-performance vectorized random number generators(RNG) for several probability distributions, convolution and correlation routines, and summary statistics
functions.
Data Fitting Library, which provides capabilities for spline-based approximation of functions, derivatives
and integrals of functions, and search.
Extended Eigensolver, a shared memory programming (SMP) version of an eigensolver based on the Feast
Eigenvalue Solver.

For details see the Intel® oneAPI Math Kernel Library Developer Reference.
Intel® oneAPI Math Kernel Library (oneMKL) is optimized for performance on Intel processors. oneMKL also
runs on non-Intel x86-compatible processors.
For Windows* and Linux* systems based on Intel® 64 Architecture, oneMKL also includes support for theIntel® Many Integrated Core Architecture (Intel® MIC Architecture) and provides libraries to help you port
your applications to Intel MIC Architecture.
NOTE
oneMKL provides limited input validation to minimize the performance overheads. It is yourresponsibility when using oneMKL to ensure that input data has the required format and does notcontain invalid characters. These can cause unexpected behavior of the library. Examples of the inputs
that may result in unexpected behavior:••

Not-a-number (NaN) and other special floating point values
Large inputs may lead to accumulator overflow

As the oneMKL API accepts raw pointers, it is your application's responsibility to validate the buffer
sizes before passing them to the library. The library requires subroutine and function parameters to bevalid before being passed. While some oneMKL routines do limited checking of parameter errors, yourapplication should check for NULL pointers, for example.

8

Introducing the Intel® oneAPI Math Kernel Library

Product and Performance Information
Performance varies by use, configuration and other factors. Learn more at www.Intel.com/
PerformanceIndex.
Notice revision #20201201

9

Intel® oneAPI Math Kernel Libraryfor Windows* Developer Guide

Notational Conventions
The following term is used in reference to the operating system.
Windows*

This term refers to information that is valid on all supported Windows* operating
systems.

The following notations are used to refer to Intel® oneAPI Math Kernel Library directories.

<parent
directory>

The installation directory that includes Intel® oneAPI Math Kernel Library directory;
for example, the directory for Intel® Parallel Studio XE Composer Edition.

<mkl directory>

The main directory where Intel® oneAPI Math Kernel Library is installed:<mkl directory>=<parent directory>\mkl.
Replace this placeholder with the specific pathname in the configuring, linking, andbuilding instructions.
The following font conventions are used in this document.
ItalicItalic is used for emphasis and also indicates document names in body text, forexample:see Intel® oneAPI Math Kernel Library Developer Reference.

Monospacelowercase mixed
with uppercase

Indicates:•

Commands and command-line options, for example,

ifort myprog.f mkl_blas95.lib mkl_c.lib libiomp5md.lib•

Filenames, directory names, and pathnames, for example,

C:\Program Files\Java\jdk1.5.0_09•

C/C++ code fragments, for example,

a = new double [SIZE*SIZE];UPPERCASE
MONOSPACE

Indicates system variables, for example, $MKLPATH.

MonospaceitalicIndicates a parameter in discussions, for example, lda.
When enclosed in angle brackets, indicates a placeholder for an identifier, an
expression, a string, a symbol, or a value, for example, <mkl directory>.
Substitute one of these items for the placeholder.

[ items ]Square brackets indicate that the items enclosed in brackets are optional.

{ item | item }Braces indicate that only one of the items listed between braces should be selected.
A vertical bar ( | ) separates the items.

10Related Information

Related Information
To reference how to use the library in your application, use this guide in conjunction with the following
documents:••

The Intel® oneAPI Math Kernel Library Developer Reference, which provides reference information on
routine functionalities, parameter descriptions, interfaces, calling syntaxes, and return values.
The Intel® oneAPI Math Kernel Library forWindows* OS Release Notes.

111Intel® oneAPI Math Kernel Libraryfor Windows* Developer Guide

Getting Started

1

Product and Performance Information
Performance varies by use, configuration and other factors. Learn more at www.Intel.com/
PerformanceIndex.
Notice revision #20201201

Shared Library Versioning
Intel® oneAPI Math Kernel Library (oneMKL) adds shared library versioning for all operating systems andplatforms, as opposed to not using any library versioning up to Intel® Math Kernel Library (Intel® MKL) 2020
Update 4.
This new feature:•••

Allows applications to work correctly in an environment with multiple oneMKL and/or Intel® MKL packages
installed
Communicates clearly when incompatible changes are made, and an application should be rebuilt
Allows you to link against a specific version of shared libraries

The starting version for shared libraries is “1” and any change that break backward compatibility will result in
an increment to this number. Intel expects to make this change as seldom as possible and inform customers
about it at least 24 months in advance.
The product version “2021.1” is now decoupled from the library version, meaning that “2021.2” can ship withshared libraries versioned as “1”. This means that the libraries shipped in “2021.2” are backward compatible
with libraries shipped in “2021.1”.
Changes to the link-line:•

No changes are required to the link-line because symbolic links are provided with the old names, which
point to the new library that contains the version information on Linux* and MacOS*. The symbolic linkname is also the soname and install_name of that library on Linux and MacOS, respectively.
•

For example, libmkl_core.so -> libmkl_core.<version>.so•••

For example, libmkl_core.dylib -> libmkl_core.<version>.dylib
Using -lmkl_core will still work as before, ensuring backward compatibility with Intel® MKL 2020 lineup (including Intel® Parallel Studio and Intel® System Studio distributions).
On Windows*, import libraries used in the link-line do not contain any version information, as before, but
point to the new DLL, which contains the version information.
•

For example, mkl_core_dll.lib has the same name as before and requires no change to the linkline. The linker, however, resolves this to the new mkl_core.<version>.dll instead of the older
mkl_core.dll.

CMake Config for oneMKL
If you want to integrate oneMKL into your CMake projects, starting with the Intel® oneAPI Math Kernel
Library (oneMKL) 2021.3 release, MKLConfig.cmake is provided as part of the package and installation.
MKLConfig.cmake supports all oneMKL configurations, compilers, and runtimes, as the oneMKL productitself. Help/usage is provided in the top section of MKLConfig.cmake.

12

Getting Started

1

Examplemy_test/|_ build/|_ CMakeLists.txt|_ app.c<-- Out-of-source build directory<-- User side project's CMakeLists.txt<-- Source file that uses oneMKL API

CMakeLists.txt

cmake_minimum_required(VERSION 3.13)
enable_testing()
project(oneMKL_Example LANGUAGES C)
find_package(MKL CONFIG REQUIRED)#message(STATUS "${MKL_IMPORTED_TARGETS}") #Provides available list of targets based on input
add_executable(myapp app.c)
target_compile_options(myapp PUBLIC $<TARGET_PROPERTY:MKL::MKL,INTERFACE_COMPILE_OPTIONS>)
target_include_directories(myapp PUBLIC$<TARGET_PROPERTY:MKL::MKL,INTERFACE_INCLUDE_DIRECTORIES>)
target_link_libraries(myapp PUBLIC $<LINK_ONLY:MKL::MKL>)
add_test(NAME mytest COMMAND myapp)if(MKL_ENV)
set_tests_properties(mytest PROPERTIES ENVIRONMENT "${MKL_ENV}")
endif()
Command line

# Source the compiler and runtime beforehandbuild$ cmake .. -G Ninja -DCMAKE_C_COMPILER=icl
build$ cmake --build . && ctest# If MKLConfig.cmake is not located by CMake automatically, its path can be manually specified
by MKL_DIR:
build$ cmake .. -G Ninja -DCMAKE_C_COMPILER=icl -DMKL_DIR=<Full path to MKLConfig.cmake>
NOTE
Ninja and NMake Makefiles are supported on Windows. Other CMake Generators may work but are notguaranteed.
When the Ninja build system is in use, Ninja 1.10.2+ is required for Fortran support.

Checking Your Installation
After installing the Intel® oneAPI Math Kernel Library (oneMKL), verify that the library is properly installed
and configured:
1.

Intel® oneAPI Math Kernel Library installs in the<parent directory> directory.
Check that the subdirectory of <parent directory> referred to as <mkl directory> was created.

2.
3.

4.

Check that subdirectories for Intel® oneAPI Math Kernel Library redistributable DLLs redist\ia32 andredist\intel64 were created in the <parent directory> directory (See redist.txtin the Intel®
oneAPI Math Kernel Library documentation directory for a list of files that can be redistributed.)
If you want to keep multiple versions of Intel® oneAPI Math Kernel Library installed on your system,
update your build scripts to point to the correct Intel® oneAPI Math Kernel Library version.
Check that the vars.bat file appears in the <mkl directory>\env directory.
Use this file to assign Intel® oneAPI Math Kernel Library-specific values to several environment
variables, as explained in Scripts to Set Environment Variables Setting Environment Variables .
To understand how the Intel® oneAPI Math Kernel Library directories are structured, seeStructure of theIntel® Math Kernel Library.

13

1
5.

Intel® oneAPI Math Kernel Libraryfor Windows* Developer Guide

To make sure that Intel® oneAPI Math Kernel Library runs on your system, launch an Intel® oneAPI Math
Kernel Library example, as explained inUsing Code Examples.

See Also
Notational Conventions

Setting Environment Variables
When the installation of Intel® oneAPI Math Kernel Library for Windows* is complete, set environment
variables using oneAPI setvars.sh. The script will source the INCLUDE, MKLROOT, LD_LIBRARY_PATH,LIBRARY_PATH, CPATH, NLSPATH, and PKG_CONFIG_PATH.
The script accepts the oneMKL-specific parameters, explained in the following table:
Setting Specified

Required(Yes/No)

Possible Values

ArchitectureYes,when applicable

intel64

Use of Intel® oneAPI
Math Kernel Library
Fortran modules
precompiled with theIntel®Visual FortrancompilerNo

mod

Programming
interface (LP64 or
ILP64)

No

lp64, default

Comment

Supply thisparameter only if you
are using thiscompiler.

ilp64

For example:•••

The command setvars ia32
sets the environment for Intel® oneAPI Math Kernel Library to use the Intel 32 architecture.
The command setvars intel64 mod ilp64
sets the environment for Intel® oneAPI Math Kernel Library to use the Intel 64 architecture, ILP64
programming interface, and Fotran modules.
The command setvars intel64 modsets the environment for Intel® oneAPI Math Kernel Library to use the Intel 64 architecture, LP64
interface, and Fotran modules.
NOTE
Supply the parameter specifying the architecture first, if it is needed. Values of the other two
parameters can be listed in any order.

See Also
High-level Directory StructureIntel® oneAPI Math Kernel Library Interface Libraries and Modules
Fortran 95 Interfaces to LAPACK and BLAS
Setting the Number of Threads Using an OpenMP* Environment Variable

Compiler Support
Intel® oneAPI Math Kernel Library supports compilers identified in theRelease Notes. However, the library has
been successfully used with other compilers as well.

14

Getting Started

1Intel® oneAPI Math Kernel Library provides the cdecl interface (default interface of the Microsoft Visual C*
application) for the IA-32 architecture.
When building Intel® oneAPI Math Kernel Library code examples, you can select a compiler:••

For Fortran examples: Intel® or PGI* compiler
For C examples: Intel, Microsoft Visual C++*, or PGI compiler

Intel® oneAPI Math Kernel Library provides a set of include files to simplify program development byspecifying enumerated values and prototypes for the respective functions. Calling Intel® oneAPI Math Kernel
Library functions from your application without an appropriate include file may lead to incorrect behavior of
the functions.

See Also
Intel® oneAPI Math Kernel Library Include Files

Using Code Examples
The Intel® oneAPI Math Kernel Library package includes code examples, located in theexamples subdirectoryof the installation directory. Use the examples to determine:•••

Whether Intel® oneAPI Math Kernel Library is working on your system
How you should call the library
How to link the library

If an Intel® oneAPI Math Kernel Library component that you selected during installation includes code
examples, these examples are provided in a separate archive. Extract the examples from the archives beforeuse.
For each component, the examples are grouped in subdirectories mainly by Intel® oneAPI Math Kernel Libraryfunction domains and programming languages. For instance, theblas subdirectory (extracted from theexamples_core archive) contains a makefile to build the BLAS examples and the vmlc subdirectory contains
the makefile to build the C examples for Vector Mathematics functions. Source code for the examples is in
the next-level sources subdirectory.

See Also
High-level Directory Structure

What You Need to Know Before You Begin Using the Intel®
oneAPI Math Kernel Library
Target platform

Identify the architecture of your target machine:••

IA-32 or compatible
Intel® 64 or compatible

Reason:Because Intel® oneAPI Math Kernel Library libraries are located indirectories corresponding to your particular architecture (seeArchitecture Support),
you should provide proper paths on your link lines (see Linking Examples).To
configure your development environment for the use with Intel® oneAPI Math Kernel
Library, set your environment variables using the script corresponding to yourarchitecture (see Scripts to Set Environment Variables Setting Environment Variables
for details).
Mathematicalproblem

Identify all Intel® oneAPI Math Kernel Library function domains that you require:
•
•
•

BLAS
Sparse BLAS
LAPACK

15

1Intel® oneAPI Math Kernel Libraryfor Windows* Developer Guide

•
•
•
•
•
•
•
•
•
•
•
•
•

PBLAS
ScaLAPACK
Sparse Solver routines
Parallel Direct Sparse Solvers for Clusters
Vector Mathematics functions (VM)
Vector Statistics functions (VS)
Fourier Transform functions (FFT)Cluster FFT
Trigonometric Transform routines
Poisson, Laplace, and Helmholtz Solver routines
Optimization (Trust-Region) Solver routines
Data Fitting Functions
Extended Eigensolver Functions

Reason: The function domain you intend to use narrows the search in the Intel®
oneAPI Math Kernel Library Developer Referencefor specific routines you need.
Additionally, if you are using the Intel® oneAPI Math Kernel Library cluster software,your link line is function-domain specific (seeWorking with the Intel® oneAPI Math
Kernel Library Cluster Software). Coding tips may also depend on the function
domain (see Other Tips and Techniques to Improve Performance).
Programming
languageIntel® oneAPI Math Kernel Library provides support for both Fortran and C/C++
programming. Identify the language interfaces that your function domains support(see Appendix A: Intel® oneAPI Math Kernel Library Language Interfaces Support).
Reason:Intel® oneAPI Math Kernel Library provides language-specific include files
for each function domain to simplify program development (seeLanguage Interfaces
Support_ by Function Domain).
For a list of language-specific interface libraries and modules and an example how to
generate them, see also Using Language-Specific Interfaces with Intel® oneAPI Math
Kernel Library.

Range of integerdata

If your system is based on the Intel 64 architecture, identify whether yourapplication performs calculations with large data arrays (of more than 231-1
elements).
Reason: To operate on large data arrays, you need to select the ILP64 interface,where integers are 64-bit; otherwise, use the default, LP64, interface, where
integers are 32-bit (see Using the ILP64 Interface vs).

Threading model

Identify whether and how your application is threaded:•••Threaded with the Intel compiler
Threaded with a third-party compiler
Not threaded

Reason:The compiler you use to thread your application determines which
threading library you should link with your application. For applications threadedwith a third-party compiler you may need to use Intel® oneAPI Math Kernel Library inthe sequential mode (for more information, seeLinking with Threading Libraries).
Number of threads

If your application uses an OpenMP* threading run-time library, determine thenumber of threads you want Intel® oneAPI Math Kernel Library to use.
Reason:By default, the OpenMP* run-time library sets the number of threads forIntel® oneAPI Math Kernel Library. If you need a different number, you have to set it
yourself using one of the available mechanisms. For more information, seeImproving
Performance with Threading.

Linking model

Decide which linking model is appropriate for linking your application with Intel®
oneAPI Math Kernel Library libraries:••

16

StaticDynamic

Getting Started

1

Reason: The link libraries for static and dynamic linking are different. For the list oflink libraries for static and dynamic models, linking examples, and other relevant
topics, like how to save disk space by creating a custom dynamic library, see Linking
Your Application with the Intel® oneAPI Math Kernel Library.
MPI used

Decide what MPI you will use with the Intel® oneAPI Math Kernel Library cluster
software. You are strongly encouraged to use the latest available version of Intel®
MPI.
Reason: To link your application with ScaLAPACK and/or Cluster FFT, the libraries
corresponding to your particular MPI should be listed on the link line (see Working
with the Intel® oneAPI Math Kernel Library Cluster Software).

17

2Intel® oneAPI Math Kernel Libraryfor Windows* Developer Guide

Structure of the Intel® oneAPI
Math Kernel Library2

Product and Performance Information
Performance varies by use, configuration and other factors. Learn more at www.Intel.com/
PerformanceIndex.
Notice revision #20201201

Architecture Support
Intel® oneAPI Math Kernel Library (oneMKL) forWindows* provides architecture-specific implementations forsupported platforms. The following table lists the supported architectures and directories where eacharchitecture-specific implementation is located.
Architecture

Location

IA-32 or compatible<mkl directory>\lib\ia32<mkl directory>\redist\ia32

Intel® 64 or compatible<mkl directory>\lib\intel64<mkl directory>\redist\intel64 (DLLs)

See Also
High-level Directory Structure
Notational Conventions
Detailed Structure of the IA-32 Architecture Directories
Detailed Structure of the Intel® 64 Architecture Directories

High-level Directory Structure
Directory

Contents<mkl directory>

Installation directory of the Intel® oneAPI Math Kernel Library (oneMKL)

Subdirectories of<mkl directory>

env\vars.bat

Source script to set environment variables

benchmarks\linpack

Shared-Memory (SMP) version of the LINPACK benchmark

benchmarks\mp_linpack

Message-passing interface (MPI) version of the LINPACK benchmark

lib\ia32

Static libraries and static interfaces to DLLs for the IA-32 architecturelib\intel64

Static libraries and static interfaces to DLLs for the Intel® 64 architecture

examples

Source and data files for Intel® oneAPI Math Kernel Library examples.
Provided in archives corresponding to Intel® oneAPI Math Kernel Librarycomponents selected during installation.

18

Structure of the Intel® oneAPI Math Kernel Library2

Directory

Contents

includeInclude files for the library routines and examples

include\ia32

Fortran 95 .mod files for the IA-32 architecture and Intel Fortran compiler

include\intel64\lp64

Fortran 95 .mod files for the Intel® 64 architecture, Intel® Fortran compiler,and LP64 interface

include\intel64\ilp64

Fortran 95 .mod files for the Intel® 64 architecture, Intel Fortran compiler,
and ILP64 interface

include\fftw

Header files for the FFTW2 and FFTW3 interfaces

include\oneapi

Header files for DPC++ interfaces.

interfaces\blas95

Fortran 95 interfaces to BLAS and a makefile to build the libraryinterfaces\fftw2x_cdft

MPI FFTW 2.x interfaces to Intel® oneAPI Math Kernel Library Cluster FFTinterfaces\fftw3x_cdft

MPI FFTW 3.x interfaces to Intel® oneAPI Math Kernel Library Cluster FFTinterfaces\fftw2xc

FFTW 2.x interfaces to the Intel® oneAPI Math Kernel Library FFT (C interface)interfaces\fftw2xf

FFTW 2.x interfaces to the Intel® oneAPI Math Kernel Library FFT (Fortraninterface)interfaces\fftw3xc

FFTW 3.x interfaces to the Intel® oneAPI Math Kernel Library FFT (C interface)interfaces\fftw3xf

FFTW 3.x interfaces to the Intel® oneAPI Math Kernel Library FFT (Fortraninterface)interfaces\lapack95

Fortran 95 interfaces to LAPACK and a makefile to build the libraryinterfaces\mklmpi

Tool to create a custom MKLMPI wrapper library (BLACS) for use in MKL MPIbased applications like Cluster Sparse Solver and Scalapack.

tools

Command-line link tool and tools for creating custom dynamically linkablelibraries

tools\builder

Tools for creating custom dynamically linkable libraries

redist\ia32

DLLs for applications running on processors with the IA-32 architecture

redist\intel64

DLLs for applications running on processors with Intel® 64 architecture

See Also
Notational Conventions
Using Code Examples

Layered Model ConceptIntel® oneAPI Math Kernel Library is structured to support multiple compilers and interfaces, both serial andmulti-threaded modes, different implementations of threading run-time libraries, and a wide range ofprocessors. Conceptually Intel® oneAPI Math Kernel Library can be divided into distinct parts to support
different interfaces, threading models, and core computations:1.
2.

Interface Layer
Threading Layer

19

2
3.

Intel® oneAPI Math Kernel Libraryfor Windows* Developer Guide

Computational Layer

You can combine Intel® oneAPI Math Kernel Library libraries to meet your needs by linking with one library ineach part layer-by-layer.
To support threading with different compilers, you also need to use an appropriate threading run-time library(RTL). These libraries are provided by compilers and are not included in Intel® oneAPI Math Kernel Library.
The following table provides more details of each layer.
Layer

Description

Interface Layer

This layer matches compiled code of your application with the threading and/or
computational parts of the library. This layer provides:•••

Threading Layercdecl interface.
LP64 and ILP64 interfaces.
Compatibility with compilers that return function values differently.

This layer:••

Provides a way to link threaded Intel® oneAPI Math Kernel Library with supported
compilers.
Enables you to link with a threaded or sequential mode of the library.

This layer is compiled for different environments (threaded or sequential) andcompilers (from Intel ).
Computational
Layer

This layer accommodates multiple architectures through identification of architecturefeatures and chooses the appropriate binary code at run time.

See Also
Using the ILP64 Interface vs. LP64 Interface
Linking Your Application with the Intel® oneAPI Math Kernel Library
Linking with Threading Libraries

20Linking Your Application with the Intel® oneAPI Math Kernel Library

Linking Your Application with
the Intel® oneAPI Math Kernel
Library3

3

Product and Performance Information
Performance varies by use, configuration and other factors. Learn more at www.Intel.com/
PerformanceIndex.
Notice revision #20201201

Linking Quick StartIntel® oneAPI Math Kernel Library (oneMKL) provides several options for quick linking of your application. The
simplest options depend on your development environment:
Intel® Parallel Studio XE Composer Edition compiler

see Using the /Qmkl Compiler Option.

Microsoft Visual Studio* Integrated Development Environment(IDE)

see Automatically Linking a Project in the
Visual Studio* Integrated Development
Environment with Intel® MKL.

Other options are independent of your development environment, but depend on the way you link:
Explicit dynamic linking

see Using the Single Dynamic Library for
how to simplify your link line.

Explicitly listing libraries on your link line

see Selecting Libraries to Link with for asummary of the libraries.

Using pkg-config tool to get compilation and link lines

see Using pkg-config metadata filesfor asummary on how to use Intel® oneAPI
Math Kernel Library pkg-config metadata
files.

Using an interactive interface

see Using the Link-line Advisor todetermine libraries and options to specify
on your link or compilation line.

Using an internally provided toolsee Using the Command-line Link Tool todetermine libraries, options, andenvironment variables or even compile andbuild your application.

Using the /Qmkl Compiler Option
The Intel®Parallel Studio XE Composer Edition compiler supports the following variants of the /Qmkl compiler
option:/Qmkl or
/Qmkl:parallel

to link with a certain Intel® oneAPI Math Kernel Library threading
layer depending on the threading option provided:••

For -qopenmp the OpenMP threading layer for Intel compilers
For -tbb the Intel® Threading Building Blocks (Intel® TBB)
threading layer213Intel® oneAPI Math Kernel Libraryfor Windows* Developer Guide/Qmkl:sequential

to link with sequential version of Intel® oneAPI Math Kernel
Library.

/Qmkl:cluster

to link with Intel® oneAPI Math Kernel Library clustercomponents (sequential) that use Intel MPI.

NOTE
The -qopenmp option has higher priority than -tbbin choosing the Intel® oneAPI Math Kernel Librarythreading layer for linking.
For more information on the /Qmkl compiler option, see the Intel Compiler User and Reference Guides.
For each variant of the /Qmkl option, the compiler links your application using the following conventions:••cdecl for the IA-32 architecture
LP64 for the Intel® 64 architecture

If you specify any variant of the /Qmkl compiler option, the compiler automatically includes the Intel® oneAPI
Math Kernel Library libraries. In cases not covered by the option, use the Link-line Advisor or see Linking in
Detail.

See Also
Intel® oneAPI DPC++/C++ Compiler Developer Guide and ReferenceIntel® Fortran Compiler Classic and Intel® Fortran Compiler Developer Guide and Reference
Using the ILP64 Interface vs. LP64 Interface
Using the Link-line Advisor
Intel® Software Documentation Library for Intel® compiler documentation
for Intel® compiler documentation

Automatically Linking a Project in the Visual Studio* Integrated Development
Environment with Intel® oneAPI Math Kernel Library
After a default installation of the Intel® oneAPI Math Kernel Library (oneMKL) or Intel® Parallel Studio XE
Composer Edition, you can easily configure your project to automatically link with Intel® oneAPI Math Kernel
Library.

Automatically Linking Your Microsoft Visual C/C++* Project with oneMKL
Configure your Microsoft Visual C/C++* project for automatic linking with Intel® oneAPI Math Kernel Libraryas follows:
1.
2.

Go to Project>Properties>Configuration Properties>Intel Performance Libraries.
Change the Use MKL property setting by selecting Parallel, Sequential, or Cluster as appropriate.

Specific Intel® oneAPI Math Kernel Library libraries that link with your application may depend on more
project settings. For details, see the documentation for Intel®Parallel Studio XE Composer Edition for C++.

See Also
Intel® Software Documentation Library for the documentation for Intel® Parallel Studio XE
Composer Edition
for the documentation for Intel® Parallel Studio XE Composer Edition

Automatically Linking Your Intel® Visual Fortran Project with oneMKL
Configure your Intel® Visual Fortran project for automatic linking with Intel® oneAPI Math Kernel Library as
follows:

22

Linking Your Application with the Intel® oneAPI Math Kernel Library

3

Go to Project > Properties > Libraries > Use Intel Math Kernel Library and select Parallel,
Sequential, or Cluster as appropriate.
Specific Intel® oneAPI Math Kernel Library libraries that link with your application may depend on more
project settings. For details see the documentation for Intel® Parallel Studio XE Composer Edition for Fortran.

See Also
Intel® Software Documentation Library for the documentation for Intel® Parallel Studio XE
Composer Edition
for the documentation for Intel® Parallel Studio XE Composer Edition

Using the Single Dynamic Library
You can simplify your link line through the use of the Intel® oneAPI Math Kernel Library Single Dynamic
Library (SDL).
To use SDL, place mkl_rt.lib on your link line. For example:

icl.exe application.c mkl_rt.lib
mkl_rt.lib is the import library for mkl_rt.dll.
SDL enables you to select the interface and threading library for Intel® oneAPI Math Kernel Library at run
time. By default, linking with SDL provides:••

Intel LP64 interface on systems based on the Intel® 64 architectureIntel threading

To use other interfaces or change threading preferences, including use of the sequential version of Intel®
oneAPI Math Kernel Library, you need to specify your choices using functions or environment variables as
explained in sectionDynamically Selecting the Interface and Threading Layer.
NOTEIntel® oneAPI Math Kernel Library SDL (mkl_rt) does not support DPC++ APIs. If yourapplication requires support of Intel® oneAPI Math Kernel Library DPC++ APIs, refer to Intel® oneAPI
Math Kernel Library Link-line Advisor to configure your link command.

Selecting Libraries to Link with
To link with Intel® oneAPI Math Kernel Library:••Choose one library from the Interface layer and one library from the Threading layer
Add the only library from the Computational layer and run-time libraries (RTL)

The following table lists Intel® oneAPI Math Kernel Library libraries to link with your application.
Interface layer

Threading layer

Computational
layer

RTL

Intel® 64
architecture,static linking

mkl_intel_
lp64.lib

mkl_intel_
thread.lib

mkl_core.lib

libiomp5md.lib

Intel® 64
architecture,dynamic linking

mkl_intel_
lp64_dll. lib

mkl_intel_
thread_dll.lib

mkl_core_dll.
liblibiomp5md.lib

Intel® Many
Integrated Core

libmkl_intel_
lp64.alibmkl_intel_
thread.alibmkl_core.a

libiomp5.so

23

3Intel® oneAPI Math Kernel Libraryfor Windows* Developer Guide

Interface layer

Threading layer

Computational
layer

RTL

libmkl_intel_
lp64.solibmkl_intel_
thread.solibmkl_core.so

libiomp5.so

Architecture(Intel® MIC
Architecture),
static linking
Intel MIC
Architecture,dynamic linking

The Single Dynamic Library (SDL) automatically links interface, threading, and computational libraries andthus simplifies linking. The following table lists Intel® oneAPI Math Kernel Library libraries for dynamic linking
using SDL. SeeDynamically Selecting the Interface and Threading Layer for how to set the interface andthreading layers at run time through function calls or environment settings.

Intel® 64 architecture

SDL

RTL

mkl_rt.lib

libiomp5md.lib††Linking with libiomp5md.lib is not required.

For exceptions and alternatives to the libraries listed above, see Linking in Detail.

See Also
Layered Model Concept
Using the Link-line Advisor
Using the /Qmkl Compiler Option
Working with the Cluster Software

Using the Link-line Advisor
Use the Intel® oneAPI Math Kernel Library Link-line Advisor to determine the libraries and options to specify
on your link or compilation line.
The latest version of the tool is available at Link Line Advisor for Intel® oneAPI Math Kernel Library. The toolis also available in the documentation directory of the product.
The Advisor requests information about your system and on how you intend to use Intel® oneAPI Math Kernel
Library (link dynamically or statically, use threaded or sequential mode, and so on). The tool automaticallygenerates the appropriate link line for your application.

See Also
High-level Directory Structure

Using the Command-line Link Tool
Use the command-line Link tool provided by Intel® oneAPI Math Kernel Library to simplify building yourapplication with Intel® oneAPI Math Kernel Library.
The tool not only provides the options, libraries, and environment variables to use, but also performs
compilation and building of your application.
The tool mkl_link_tool.exe is installed in the <mkl_directory>\bin<arch> directory, and supports themodes described in the following table.

24

Linking Your Application with the Intel® oneAPI Math Kernel Library

3Intel oneMKL Command-line Link Tool Modes
Mode

Description

Usage

ExampleInquiry

The tool returns the compiler
options, libraries, or
environment variables
necessary to build andexecute the application.

Get Intel® oneAPI Math
Kernel Library libraries

mkl_link_tool -libs[Intel oneMKL Link
Tool options]Get compilation options

mkl_link_tool -opts[Intel oneMKL Link
Tool options]Get environment
variables for application
executable

mkl_link_tool -env
[Intel oneMKL Link
Tool options]—mkl_link_tool[options]<compiler>[options2] file1[file2 ...]

Compilation

The Intel® oneAPI Math
Kernel Library Link Tool builds
the application.

where:•••

Interactive

Allows you to go through all
possible Intel® oneAPI Math
Kernel Library Link Tool
supported options. The
output provides libraries,
options, or environment
variables as in the inquiry
mode, or a built application
as in the compilation mode
(depending on what you
specify).

—

options represents

any number of Link
Tool options
compiler represents
the compiler name:
ifort, icc (icpc, icl),
cl, gcc (g++),gfortran, pgcc
(pgcp), pgf77 (pgf90,
pgf95, pgfortran),
mpiic, mpiifort, mpic
(mpic++), mpif77(mpif90, mpif95),
dpcpp
options2 represents
any number ofcompiler options

mkl_link_tool interactive

Use the -help option for full help with the Intel® oneAPI Math Kernel Library Link Tool and to show thedefaults for the current system.

25

3Intel® oneAPI Math Kernel Libraryfor Windows* Developer Guide

Linking Examples
See Also
Using the Link-line Advisor
Examples for Linking with ScaLAPACK and Cluster FFT

Linking on IA-32 Architecture Systems
The following examples illustrate linking that uses Intel(R) compilers.
Most examples use the .f Fortran source file. C/C++ users should instead specify a .cpp (C++) or .c (C)file and replace ifort with icl:••

Static linking of myprog.f and OpenMP* threadedIntel® oneAPI Math Kernel Library supporting the cdecl
interface:

ifort myprog.f mkl_intel_c.lib mkl_intel_thread.lib mkl_core.lib libiomp5md.lib
Dynamic linking of myprog.f and OpenMP* threadedIntel® oneAPI Math Kernel Library supporting thecdecl interface:

ifort myprog.f mkl_intel_c_dll.lib mkl_intel_thread_dll.lib mkl_core_dll.lib
libiomp5md.lib• Static linking of myprog.f and sequential version of Intel® oneAPI Math Kernel Library supporting thecdecl interface:••

ifort myprog.f mkl_intel_c.lib mkl_sequential.lib mkl_core.lib

Dynamic linking of myprog.f and sequential version of Intel® oneAPI Math Kernel Library supporting thecdecl interface:

ifort myprog.f mkl_intel_c_dll.lib mkl_sequential_dll.lib mkl_core_dll.lib
Static linking of myprog.f, Fortran BLAS and 95 LAPACK interfaces, and OpenMP* threadedIntel® oneAPI

Math Kernel Library supporting the cdecl interface:

ifort myprog.f mkl_lapack95.lib mkl_intel_c.lib mkl_intel_thread.lib mkl_core.lib
libiomp5md.lib• Static linking of myprog.c and Intel® oneAPI Math Kernel Library threaded with Intel® Threading Building
Blocks (Intel® TBB), provided that the LIB environment variable contains the path to Intel TBB library:icl myprog.c /link /libpath:$MKLPATH -I$MKLINCLUDE mkl_intel.lib mkl_tbb_thread.lib
mkl_core.lib tbb12.lib /MD• Dynamic linking of myprog.c and Intel® oneAPI Math Kernel Library threaded with Intel® TBB, provided
that the LIB environment variable contains the path to Intel® TBB library:icl myprog.c /link /libpath:$MKLPATH -I$MKLINCLUDE mkl_intel_dll.lib
mkl_tbb_thread_dll.lib mkl_core_dll.lib tbb12.lib /MD

See Also
Fortran 95 Interfaces to LAPACK and BLAS
Examples for linking a C application using cluster components
Examples for linking a Fortran application using cluster components
Using the Single Dynamic Library

Linking on Intel(R) 64 Architecture Systems
The following examples illustrate linking that uses Intel(R) compilers.
Most examples use the .f Fortran source file. C/C++ users should instead specify a .cpp (C++) or .c (C)file and replace ifort with icl:

26

Linking Your Application with the Intel® oneAPI Math Kernel Library•

3

Static linking of myprog.f and OpenMP* threadedIntel® oneAPI Math Kernel Library supporting the LP64
interface:

ifort myprog.f mkl_intel_lp64.lib mkl_intel_thread.lib mkl_core.lib
libiomp5md.lib• Dynamic linking of myprog.f and OpenMP* threadedIntel® oneAPI Math Kernel Library supporting theLP64 interface:

ifort myprog.f mkl_intel_lp64_dll.lib mkl_intel_thread_dll.lib mkl_core_dll.lib
libiomp5md.lib• Static linking of myprog.f and sequential version of Intel® oneAPI Math Kernel Library supporting theLP64 interface:••

ifort myprog.f mkl_intel_lp64.lib mkl_sequential.lib mkl_core.lib

Dynamic linking of myprog.f and sequential version of Intel® oneAPI Math Kernel Library supporting theLP64 interface:

ifort myprog.f mkl_intel_lp64_dll.lib mkl_sequential_dll.lib mkl_core_dll.lib
Static linking of myprog.f and OpenMP* threadedIntel® oneAPI Math Kernel Library supporting the ILP64
interface:•

ifort myprog.f mkl_intel_ilp64.lib mkl_intel_thread.lib mkl_core.lib libiomp5md.lib
Dynamic linking of myprog.f and OpenMP* threadedIntel® oneAPI Math Kernel Library supporting the

ILP64 interface:

ifort myprog.f mkl_intel_ilp64_dll.lib mkl_intel_thread_dll.lib mkl_core_dll.lib
libiomp5md.lib• Dynamic linking of user code myprog.f and OpenMP* threadedor sequential Intel® oneAPI Math Kernel

Librarysupporting the LP64 or ILP64 interface (Call appropriate functions or set environment variables tochoose threaded or sequential mode and to set the interface):•

ifort myprog.f mkl_rt.lib
Static linking of myprog.f, Fortran BLAS and 95 LAPACK interfaces, and OpenMP* threadedIntel® oneAPI
Math Kernel Library supporting the LP64 interface:

ifort myprog.f mkl_lapack95_lp64.lib mkl_intel_lp64.lib mkl_intel_thread.lib
mkl_core.lib libiomp5md.lib• Static linking of myprog.c and Intel® oneAPI Math Kernel Library threaded with Intel® Threading Building
Blocks (Intel® TBB), provided that the LIB environment variable contains the path to Intel TBB library:icl myprog.c /link /libpath:%MKLPATH% -I%MKLINCLUDE% mkl_intel_lp64.lib
mkl_tbb_thread.lib mkl_core.lib tbb12.lib /MD• Dynamic linking of myprog.c and Intel® oneAPI Math Kernel Library threaded with Intel® TBB, provided
that the LIB environment variable contains the path to Intel® TBB library:icl myprog.c /link /libpath:%MKLPATH% -I%MKLINCLUDE% mkl_intel_lp64_dll.lib
mkl_tbb_thread_dll.lib mkl_core_dll.lib tbb12.lib /MD

See Also
Fortran 95 Interfaces to LAPACK and BLAS
Examples for linking a C application using cluster components
Examples for linking a Fortran application using cluster components
Using the Single Dynamic Library

Linking in DetailThis section recommends which libraries to link with depending on your Intel® oneAPI Math Kernel Library
usage scenario and provides details of the linking.

27

3Intel® oneAPI Math Kernel Libraryfor Windows* Developer Guide

Dynamically Selecting the Interface and Threading Layer
The Single Dynamic Library (SDL) enables you to dynamically select the interface and threading layer forIntel® oneAPI Math Kernel Library.

Setting the Interface Layer
To set the interface layer at run time, use the mkl_set_interface_layer function or the
MKL_INTERFACE_LAYER environment variable.
Available interface layers depend on the architecture of your system.
On systems based on the Intel® 64 architecture, LP64 and ILP64 interfaces are available. The following tableprovides values to be used to set each interface layer.
Specifying the Interface Layer
Interface Layer

Value of

Value of the Parameter of

MKL_INTERFACE_LAYER

mkl_set_interface_layerIntel LP64, default

LP64

MKL_INTERFACE_LP64

Intel ILP64

ILP64

MKL_INTERFACE_ILP64

If the mkl_set_interface_layer function is called, the environment variable MKL_INTERFACE_LAYER is
ignored.
See the Intel® oneAPI Math Kernel Library Developer Reference for details of themkl_set_interface_layer function.
On systems based on the IA-32 architecture, the cdecl interface is available.

Setting the Threading Layer
To set the threading layer at run time, use the mkl_set_threading_layer function or the
MKL_THREADING_LAYER environment variable. The following table lists available threading layers along withthe values to be used to set each layer.
Specifying the Threading Layer
Threading Layer

Value of

Value of the Parameter of

MKL_THREADING_LAYER

mkl_set_threading_layerIntel threading,default

INTEL

MKL_THREADING_INTEL

Sequential modeof Intel® oneAPI
Math Kernel
Library

SEQUENTIAL

MKL_THREADING_SEQUENTIALPGI threading†

PGI

MKL_THREADING_PGIIntel TBB
threading

TBB

MKL_THREADING_TBB† Not supported by the SDL for Intel® Many Integrated Core Architecture.

If the mkl_set_threading_layer function is called, the environment variable MKL_THREADING_LAYER is
ignored.

28

Linking Your Application with the Intel® oneAPI Math Kernel Library3

See the Intel® oneAPI Math Kernel Library Developer Reference for details of themkl_set_threading_layer function.

Replacing Error Handling and Progress Information Routines
You can replace the Intel® oneAPI Math Kernel Library error handling routinexerbla or progress information
routine mkl_progress with your own function. If you are using SDL, to replace xerbla or mkl_progress,
call the mkl_set_xerbla and mkl_set_progress function, respectively. See the Intel® oneAPI Math Kernel
Library Developer Reference for details.
NOTE
If you are using SDL, you cannot perform the replacement by linking the object file with yourimplementation of xerbla or mkl_progress.

See Also
Using the Single Dynamic Library
Layered Model Concept
Directory Structure in Detail

Linking with Interface Libraries
Using the ILP64 Interface vs. LP64 Interface
The Intel® oneAPI Math Kernel Library ILP64 libraries use the 64-bit integer type (necessary for indexinglarge arrays, with more than 231-1 elements), whereas the LP64 libraries index arrays with the 32-bit integer
type.
The LP64 and ILP64 interfaces are implemented in the Interface layer. Link with the following interface
libraries for the LP64 or ILP64 interface, respectively:••

mkl_intel_lp64.lib or mkl_intel_ilp64.lib for static linking
mkl_intel_lp64_dll.lib or mkl_intel_ilp64_dll.lib for dynamic linking

The ILP64 interface provides for the following:••

Support large data arrays (with more than 231-1 elements)
Enable compiling your Fortran code with the /4I8 compiler option

The LP64 interface provides compatibility with the previous Intel® oneAPI Math Kernel Library versions
because "LP64" is just a new name for the only interface that the Intel® oneAPI Math Kernel Library versions
lower than 9.1 provided. Choose the ILP64 interface if your application uses Intel® oneAPI Math Kernel
Library for calculations with large data arrays or the library may be used so in the future.
On 64-bit platforms, selected domains provide API extensions with the _64 suffix (for example, SGEMM_64)
for supporting large data arrays in the LP64 library. This enables you to mix data types in one application.
The selected domains and APIs include the following:••

BLAS: Fortran-style APIs for C applications and CBLAS APIs with integer arguments
LAPACK: Fortran-style APIs for C applications and LAPACKE APIs with integer arguments

Intel® oneAPI Math Kernel Library provides the same include directory for the ILP64 and LP64 interfaces.

Compiling for LP64/ILP64
The table below shows how to compile for the ILP64 and LP64 interfaces:293Intel® oneAPI Math Kernel Libraryfor Windows* Developer Guide

FortranCompiling for
ILP64

ifort /4I8 /I<mkl directory>\include ...

Compiling for LP64

ifort /I<mkl directory>\include ...

C or C++
Compiling for
ILP64

icl /DMKL_ILP64 /I<mkl directory>\include ...

Compiling for LP64

icl /I<mkl directory>\include ...

CautionLinking of an application compiled with the /4I8 or /DMKL_ILP64 option to the LP64 libraries may
result in unpredictable consequences and erroneous output.

Coding for ILP64
You do not need to change existing code if you are not using the ILP64 interface.
To migrate to ILP64 or write new code for ILP64, use appropriate types for parameters of the Intel® oneAPI
Math Kernel Library functions and subroutines:
Integer Types

FortranC or C++

32-bit integers

INTEGER*4 or
INTEGER(KIND=4)

int

INTEGER

MKL_INT

Universal integers for ILP64/
LP64:
•
•

64-bit for ILP64
32-bit otherwise

Universal integers for ILP64/
LP64:•

without specifying KINDINTEGER*8 or
INTEGER(KIND=8)

MKL_INT64

INTEGERMKL_LONG

64-bit integers

FFT interface integers for ILP64/
LP64

without specifying KIND

To determine the type of an integer parameter of a function, use appropriate include files. For functions that
support only a Fortran interface, use the C/C++ include files *.h.
The above table explains which integer parameters of functions become 64-bit and which remain 32-bit for
ILP64. The table applies to most Intel® oneAPI Math Kernel Library functions except some Vector
Mathematics and Vector Statistics functions, which require integer parameters to be 64-bit or 32-bit
regardless of the interface:••

Vector Mathematics: The mode parameter of the functions is 64-bit.
Random Number Generators (RNG):
All discrete RNG except viRngUniformBits64 are 32-bit.
The viRngUniformBits64 generator function and vslSkipAheadStream service function are 64-bit.

30

Linking Your Application with the Intel® oneAPI Math Kernel Library•3

Summary Statistics: The estimate parameter of the vslsSSCompute/vsldSSCompute function is 64bit.

Refer to the Intel® oneAPI Math Kernel Library Developer Reference for more information.
To better understand ILP64 interface details, see also examples.

Limitations
All Intel® oneAPI Math Kernel Library function domains support ILP64 programming but FFTW interfaces toIntel® oneAPI Math Kernel Library:••

FFTW 2.x wrappers do not support ILP64.
FFTW 3.x wrappers support ILP64 by a dedicated set of functions plan_guru64.

See Also
High-level Directory StructureIntel® oneAPI Math Kernel Library Include Files
Language Interfaces Support, by Function Domain
Layered Model Concept
Directory Structure in Detail

Linking with Fortran 95 Interface Libraries
The mkl_blas95*.lib and mkl_lapack95*.liblibraries contain Fortran 95 interfaces for BLAS and
LAPACK, respectively, which are compiler-dependent. In the Intel® oneAPI Math Kernel Library package, they
are prebuilt for the Intel® Fortran compiler. If you are using a different compiler, build these libraries before
using the interface.

See Also
Fortran 95 Interfaces to LAPACK and BLAS
Compiler-dependent Functions and Fortran 90 Modules

Linking with Threading Libraries
Intel® oneAPI Math Kernel Library threading layer defines how Intel® oneAPI Math Kernel Library functions
utilize multiple computing cores of the system that the application runs on. You must link your application
with one appropriate Intel® oneAPI Math Kernel Library library in this layer, as explained below. Depending onwhether this is a threading or a sequential library, Intel® oneAPI Math Kernel Library runs in a parallel orsequential mode, respectively.
In the parallel mode, Intel® oneAPI Math Kernel Library utilizes multiple processor cores available on yoursystem, uses the OpenMP*or Intel TBB threading technology, and requires a proper threading runtime library(RTL) to be linked with your application. Independently of use of Intel® oneAPI Math Kernel Library, theapplication may also require a threading RTL. You should link not more than one threading RTL to yourapplication. Threading RTLs are provided by your compiler. Intel® oneAPI Math Kernel Library provides several
threading libraries, each dependent on the threading RTL of a certain compiler, and your choice of the Intel®
oneAPI Math Kernel Library threading library must be consistent with the threading RTL that you use in yourapplication.
The OpenMP RTL of the Intel® compiler is the libiomp5md.lib library, located under <parent directory>\compiler\lib. You can find additional information about the Intel OpenMP RTL at https://www.openmprtl.org.
The Intel TBB RTL of the Intel® compiler is the tbb12.lib library, located under <parent directory>\tbb\lib. You can find additional information about the Intel TBB RTL at https://www.threadingbuildingblocks.org.

31

3Intel® oneAPI Math Kernel Libraryfor Windows* Developer Guide

In the sequential mode, Intel® oneAPI Math Kernel Library runs unthreaded code, does not require an
threading RTL, and does not respond to environment variables and functions controlling the number of
threads. Avoid using the library in the sequential mode unless you have a particular reason for that, such as
the following:•••Your application needs a threading RTL that none of Intel® oneAPI Math Kernel Library threading libraries
is compatible withYour application is already threaded at a top level, and using parallel Intel® oneAPI Math Kernel Library
only degrades the application performance by interfering with that threadingYour application is intended to be run on a single thread, like a message-passing interface (MPI)
application

It is critical to link the application with the proper RTL. The table below explains what library in the Intel®
oneAPI Math Kernel Library threading layer and whatthreading RTL you should choose under different
scenarios:
Application

Intel® oneAPI Math Kernel Library

Uses
OpenMP

Compiled
with

Execution Mode

Threading Layernoany compiler

parallelStatic linking:

RTL Required

libiomp5md.lib

mkl_intel_
thread.lib
Dynamic linking:mkl_intel_
thread_dll.lib
noany compiler

parallelStatic linking:tbb12.lib

mkl_tbb_
thread.lib
Dynamic linking:mkl_tbb_
thread_dll.lib
noany compiler

sequential

Static linking:

none

mkl_
sequential.lib
Dynamic linking:mkl_
sequential_dll.lib
yesIntel compiler

parallelStatic linking:libiomp5md.lib

mkl_intel_
thread.lib
Dynamic linking:mkl_intel_
thread_dll.lib
yes

PGI*
compilerparallelStatic linking:mkl_pgi_
thread.lib
Dynamic linking:mkl_pgi_

32

PGI OpenMP RTL

Linking Your Application with the Intel® oneAPI Math Kernel Library

Application
Uses
OpenMPIntel® oneAPI Math Kernel LibraryCompiled
with

Execution Mode3

RTL Required

Threading Layerthread_dll.lib
yes

any othercompilerparallelNot supported. UseIntel® oneAPI Math
Kernel Library in thesequential mode.

See Also
Layered Model Concept
Notational Conventions

Linking with Computational Libraries
If you are not using the Intel® oneAPI Math Kernel Library ScaLAPACK and Cluster Fast Fourier Transforms(FFT), you need to link your application with only one computational library, depending on the linking
method:Static Linking

Dynamic Linking

mkl_core.lib

mkl_core_dll.lib

Computational Libraries for Applications that Use ScaLAPACK or Cluster FFT
ScaLAPACK and Cluster FFT require more computational libraries, which may depend on your architecture.
The following table lists computational libraries for IA -32 architecture applications that use ScaLAPACK or
Cluster FFT.
Computational Libraries for IA-32 Architecture
Function domain

Static Linking

Dynamic Linking

ScaLAPACK †mkl_scalapack_core.lib

mkl_scalapack_core_dll.lib

mkl_core.lib

mkl_core_dll.lib

mkl_cdft_core.lib

mkl_cdft_core_dll.lib

mkl_core.lib

mkl_core_dll.lib

Cluster Fourier
Transform
Functions†† Also add the library with BLACS routines corresponding to the MPI used.

The following table lists computational libraries for Intel® 64 or Intel® Many Integrated Core Architecture
applications that use ScaLAPACK or Cluster FFT.
Computational Libraries for the Intel® 64 or Intel® Many Integrated Core Architecture
Function domain

Static Linking

Dynamic Linking

ScaLAPACK, LP64
interface‡mkl_scalapack_lp64.lib

mkl_scalapack_lp64_dll.lib

mkl_core.lib

mkl_core_dll.lib

ScaLAPACK, ILP64
interface‡mkl_scalapack_ilp64.lib

mkl_scalapack_ilp64_dll.lib

mkl_core.lib

mkl_core_dll.lib

333Intel® oneAPI Math Kernel Libraryfor Windows* Developer Guide

Function domain

Static Linking

Dynamic Linking

Cluster Fourier
Transform
Functions‡mkl_cdft_core.lib

mkl_cdft_core_dll.lib

mkl_core.lib

mkl_core_dll.lib‡ Also add the library with BLACS routines corresponding to the MPI used.

See Also
Linking with ScaLAPACK and Cluster FFT
Using the Link-line Advisor
Using the ILP64 Interface vs. LP64 Interface

Linking with Compiler Run-time Libraries
Dynamically link libiomp5 or tbb library even if you link other libraries statically.
Linking to the libiomp5 statically can be problematic because the more complex your operating environment
or application, the more likely redundant copies of the library are included. This may result in performanceissues (oversubscription of threads) and even incorrect results.
To link libiomp5 or tbb dynamically, be sure the PATH environment variable is defined correctly.
Sometimes you may improve performance of your application with threaded Intel® oneAPI Math Kernel
Library by using the/MT compiler option. The compiler driver will pass the option to the linker and the latter
will load multi-thread (MT) static run-time libraries.
However, to link a Vector Mathematics (VM) application that uses the errno variable for error reporting,compile and link your code using the option that depends on the linking model:••/MTfor linking with static Intel® oneAPI Math Kernel Library libraries/MDfor linking with dynamic Intel® oneAPI Math Kernel Library libraries

See Also
Setting Environment Variables
Layered Model Concept

Linking with System Libraries
If your system is based on the Intel® 64architecture, be aware that Microsoft SDK builds 1289 or higher
provide the bufferoverflowu.lib library to resolve the __security_cookie external references.
Makefiles for examples and tests include this library by using the buf_lib=bufferoverflowu.lib macro. If
you are using older SDKs, leave this macro empty on your command line as follows: buf_lib= .

See Also
Linking Examples

Building Custom Dynamic-link Libraries
Сustom dynamic-link libraries (DLL)reduce the collection of functions available in Intel® oneAPI Math Kernel
Library libraries to those required to solve your particular problems, which helps to save disk space and build
your own dynamic libraries for distribution.
The Intel® oneAPI Math Kernel Library customDLL builder enables you to create a dynamic library containing
the selected functions and located in the tools\builder directory. The builder contains a makefile and adefinition file with the list of functions.

34

Linking Your Application with the Intel® oneAPI Math Kernel Library

3

Using the Custom Dynamic-link Library Builder in the Command-line ModeTo build a custom DLL, use the following command:

nmake target [<options>]
The following table lists possible values of target and explains what the command does for each value:
Value

Comment

libia32

The builder uses static Intel® oneAPI Math Kernel Library interface, threading, andcore libraries to build a customDLL for the IA-32 architecture.

libintel64

The builder uses static Intel® oneAPI Math Kernel Library interface, threading, andcore libraries to build a customDLL for the Intel® 64 architecture.

dllia32

The builder uses the single dynamic library libmkl_rt.dll to build a custom DLL
for the IA-32 architecture.

dllintel64

The builder uses the single dynamic library libmkl_rt.dll to build a custom DLL
for the Intel® 64 architecture.

help

The command prints Help on the custom DLL builder

The <options> placeholder stands for the list of parameters that define macros to be used by the makefile.
The following table describes these parameters:
Parameter[Values]

Description

interface

Defines which programming interface to use.Possible values:••

threading ={parallel|sequential}
Prallel ={intel|tbb}
cluster = {yes|no}blacs_mpi ={intelmpi|
msmpi}
blacs_name =<lib name>For the IA-32 architecture, {cdecl}. The default value is cdecl.
For the Intel 64 architecture, {lp64|ilp64}. The default value is lp64.

Defines whether to use the Intel® oneAPI Math Kernel Library in the threaded or
sequential mode. The default value isparallel.
Specifies whether to use Intel OpenMP or Intel® oneTBB. The default value isintel.
(For libintel64only) Specifies whether Intel® oneAPI Math Kernel Library clustercomponents (BLACS, ScaLAPACK and/or CDFT) are needed to build the custom
shared object. The default value isno.
Specifies the pre-compiled Intel® oneAPI Math Kernel Library BLACS library to use.
Ignored if'cluster=no'. The default value is intelmpi.
Specifies the name (without extension) of a custom Intel® oneAPI Math Kernel
Library BLACS library to use. Ignored if'cluster=no'. 'blacs_mpi' is ignored if'blacs_name' was explicitly specified. The default value is
mkl_blacs_<blacs_mpi>_<interface>.

mpi = <libname>Specifies the name (without extension) of the MPI library used to build the custom
DLL. Ignored if 'cluster=no'. The default value is impi.

export =<file name>Specifies the full name of the file that contains the list of entry-point functions to be
included in the DLL. The default name is user_example_list (no extension).

name = <dll
name>Specifies the name of the dll and interface library to be created. By default, thenames of the created libraries are mkl_custom.dll and mkl_custom.lib.

353Intel® oneAPI Math Kernel Libraryfor Windows* Developer Guide

Parameter[Values]

Description

xerbla =<error handler>

Specifies the name of the object file <user_xerbla>.objthat contains the error
handler of the user. The makefile adds this error handler to the library for use
instead of the default Intel® oneAPI Math Kernel Library error handlerxerbla. If youomit this parameter, the native Intel® oneAPI Math Kernel Libraryxerbla is used.
See the description of the xerblafunction in the Intel® oneAPI Math Kernel Library
Developer Reference to develop your own error handler. For the IA-32 architecture,
the object file should be in the interface defined by the interface macro (cdecl).

MKLROOT =<mkl directory>Specifies the location of Intel® oneAPI Math Kernel Library libraries used to build thecustomDLL. By default, the builder uses the Intel® oneAPI Math Kernel Library
installation directory.

uwd_compat ={yes|no}Build a Universal Windows Driver (UWD)-compatible custom DLL with OneCore.lib.
The recommended versions of Windows SDK are 10.0.17134.0 or higher.
If 'uwd_compat'=yes, then threading = sequential and crt = ucrt.lib bydefault.
The default value of is uwd_compat is no.

buf_lib

Manages resolution of the __security_cookie external references in the custom
DLL on systems based on the Intel® 64 architecture.
By default, the makefile uses the bufferoverflowu.lib library of Microsoft SDK
builds 1289 or higher. This library resolves the __security_cookie externalreferences.
To avoid using this library, set the empty value of this parameter. Therefore, if you
are using an older SDK, set buf_lib= .
CautionUse the buf_lib parameter only with the empty value. Incorrect value of the parameter
causes builder errors.

crt = <c runtime library>
manifest ={yes|no|embed}

Specifies the name of the Microsoft C run-time library to be used to build the custom
DLL. By default, the builder uses msvcrt.lib.
Manages the creation of a Microsoft manifest for the custom DLL:•••

If manifest=yes, the manifest file with the name defined by the name parameter
above and the manifest extension is created.
If manifest=no, the manifest file is not be created.
If manifest=embed, the manifest is embedded into the DLL.

By default, the builder does not use the manifest parameter.
All of the above parameters are optional. However, you must make the system and c-runtime (crt) libraries
and link.exe available by setting the PATH and LIB environment variables appropriately. You can do this in
the following ways:•••

Manually
If you are using Microsoft Visual Studio (VS), call the vcvarsall.bat script with the appropriate 32-bit(x86) or 64-bit (x64 or amd-64) architecture flag.
If you are using the Intel compiler, use the compilervars.bat script with the appropriate 32-bit (x86) or
64-bit (x64 or amd-64) architecture flag.

36

Linking Your Application with the Intel® oneAPI Math Kernel Library

3

In the simplest case, the command line is:#source Visual Studio environment variables
call vcvarsall.bat x86#run custom dll builder script
nmake ia32
and the missing options have default values. This command creates the mkl_custom.dll andmkl_custom.lib libraries with the cdecl interface for processors using the IA-32 architecture . The
command takes the list of functions from the functions_listfile and uses the native Intel® oneAPI Math
Kernel Library error handlerxerbla.
Here is an example of a more complex case:#source Visual Studio environment variables
call vcvarsall.bat x86#run custom dll builder script
nmake ia32 interface=cdecl export=my_func_list.txt name=mkl_small xerbla=my_xerbla.obj
In this case, the command creates the mkl_small.dll and mkl_small.lib libraries with the cdecl interface
for processors using the IA-32 architecture. The command takes the list of functions frommy_func_list.txt file and uses the error handler of the user my_xerbla.obj.
To build a UWD-compatible custom dll, use the uwd_compat=yes option. For this purpose, you must make adifferent set of universal system (OneCore.lib) and universal c-runtime (ucrt.lib) libraries available. You
can get these libraries by downloading Windows 10 SDK 10.0.17134.0 (version 1803) or newer. Make sure tosource the Visual Studio environment with the appropriate native architecture to add the libraries to yourpath.
This example shows how to create a 64-bit architecture library, mkl_uwd_compat.dll, that is UWDcompatible with the lp64 interface using my_function_list.txt for specific functionality:#source Visual Studio environment variables, LIB should have paths to desired OneCore.lib anduniversal crt libraries
call vcvarsall.bat x64#run custom dll builder script
nmake intel64 interface=lp64 export=my_func_list.txt uwd_compat=yes name=mkl_uwd_compat

See Also
Linking with System Libraries

Composing a List of Functions
To compose a list of functions for a minimal custom DLL needed for your application, you can use thefollowing procedure:
1.
2.

Link your application with installed Intel® oneAPI Math Kernel Library libraries to make sure theapplication builds.
Remove all Intel® oneAPI Math Kernel Library libraries from the link line and start linking.

3.

Unresolved symbols indicate Intel® oneAPI Math Kernel Library functions that your application uses.
Include these functions in the list.
Important
Each time your application starts using more Intel® oneAPI Math Kernel Library functions, update thelist to include the new functions.

37

3Intel® oneAPI Math Kernel Libraryfor Windows* Developer Guide

See Also
Specifying Function Names

Specifying Function Names
In the file with the list of functions for your custom DLL, adjust function names to the required interface. For
example, you can list the cdecl entry points as follows:

DGEMM
DTRSM
DDOT
DGETRF
DGETRScblas_dgemm
cblas_ddot
For more examples, see domain-specific lists of function names in the <mkl directory>\tools\builder
folder. This folder contains lists of function names for cdecl interfaces.
NOTE
The lists of function names are provided in the <mkl directory>\tools\builder folder merely as
examples. See Composing a List of Functions for how to compose lists of functions for your custom
DLL.

Tip
Names of Fortran-style routines (BLAS, LAPACK, etc.) can be both upper-case or lower-case, with orwithout the trailing underscore. For example, these names are equivalent:
BLAS: dgemm, DGEMM, dgemm_, DGEMM_
LAPACK: dgetrf, DGETRF, dgetrf_, DGETRF_.
Properly capitalize names of C support functions in the function list. To do this, follow the guidelines below:
1.
2.

In the mkl_service.h include file, look up a #define directive for your function(mkl_service.h is included in the mkl.h header file).
Take the function name from the replacement part of that directive.

For example, the #define directive for the mkl_disable_fast_mm function is#define mkl_disable_fast_mm MKL_Disable_Fast_MM.
Capitalize the name of this function in the list like this: MKL_Disable_Fast_MM.
For the names of the Fortran support functions, see the tip.

Building a Custom Dynamic-link Library in the Visual Studio* Development System
You can build a custom dynamic-link library (DLL) in the Microsoft Visual Studio* Development System(VS*). To do this, use projects available in the tools\builder\MSVS_Projectssubdirectory of the Intel®
oneAPI Math Kernel Library directory. The directory contains subdirectories with projects for the respective
versions of the Visual Studio Development System, for example,VS2012. For each version of VS two solutions
are available:••

libia32.sln builds a custom DLL for the IA-32 architecture.
libintel64.sln builds a custom DLL for the Intel® 64 architecture.

The builder uses the following default settings for the custom DLL:

38

Linking Your Application with the Intel® oneAPI Math Kernel Library

Interface:cdecl for the IA-32 architecture and LP64 for the Intel 64architecture

Error handler:

Native Intel® oneAPI Math Kernel Libraryxerbla

Create Microsoft manifest:

yes

List of functions:in the project's source file examples.def

3

To build a custom DLL:
1.
2.

Set the MKLROOTenvironment variable with the installation directory of the Intel® oneAPI Math Kernel
Library version you are going to use.
Open the libia32.sln or libintel64.sln solution depending on the architecture of your system.
The solution includes the following projects:3.

• i_malloc_dll• vml_dll_core• cdecl_parallel (in libia32.sln) or lp64_parallel (in libintel64.sln)• cdecl_sequential (in libia32.sln) or lp64_sequential (in libintel64.sln)[Optional] To change any of the default settings, select the project depending on whether the DLL willuse Intel® oneAPI Math Kernel Library functions in the sequential or multi-threaded mode:4.

• In the libia32 solution, select the cdecl_sequential or cdecl_parallel project.
• In the libintel64 solution, select the lp64_sequential or lp64_parallel project.
[Optional] To include your own error handler in the DLL:

5.

a.
Select Project>Properties>Configuration Properties>Linker>Input.
b.
Add <user_xerbla>.obj[Optional] To turn off creation of the manifest:a.

6.

Select Project>Properties>Configuration Properties>Linker>Manifest File>Generate
Manifest.
b.
Select: no.
[Optional] To change the list of functions to be included in the DLL:

7.

a.
Select Source Files.
b.
Edit the examples.def file. Refer to Specifying Function Names for how to specify entry points.
To build the library, select Build>Build Solution.

See Also
Using the Custom Dynamic-link Library Builder in the Command-line Mode

Distributing Your Custom Dynamic-link Library
To enable use of your custom DLL in a threaded mode, distribute libiomp5md.dll along with the custom
DLL.

Building a Universal Windows DriverIntel® oneAPI Math Kernel Library is compatible with Universal Windows Drivers (UWDs) whose binaries are
Win10 SDK version 1803 (also called Redstone 4, RS4 or Win 10 SDK build 10.0.17134.0) or higher. The
Windows system calls used in the statically linked libraries are defined in:••

OneCore.Lib for headless applications like Nano Server.
OneCoreUAP.Lib for a UWD.

A Windows application or driver that links to either of these libraries can use functionality from staticallylinked Intel® oneAPI Math Kernel Library to accelerate computations. For more information on design criteria
for a UWD, see the Microsoft website.

39

3Intel® oneAPI Math Kernel Libraryfor Windows* Developer Guide

This table summarizes important information about compatibility between Intel® oneAPI Math Kernel Libraryand UWDs.
Compatibility between Intel MKL and Universal Windows Driver (UWD)
Criteria

Compatibility

SDK

10.0.17134.0 (also called Redstone 4, RS4, version 1803) or higher

Architecture

•
•

ia32
intel64

Universal API sets••

OneCore.Lib for headless applications like Nano Server
OneCoreUAP.Lib for a UWD

Threading layers••

Sequential
Intel® Threading Building Blocks (Intel® TBB). See Using Intel®TBB
in UWP Applications.

UWD Compliance••Intel® oneAPI Math Kernel Library with static linking libraries
For dynamic linking libraries, use the custom DLL builder with
uwd_compat=yes to construct a custom DLL that is compatible
with UWD. See Using the Custom DLL Builder.

NOTE The UWD links to Intel® oneAPI Math Kernel Library using the same link line settings as drivers
or executables that are not universal. See Link Line Advisor for recommended linking models.

UWD Compliance for 32-bit Architecture (ia32)
These tables describe compliance between Intel® oneAPI Math Kernel Library and UWD on 32-bit (ia32)
architecture for static and dynamic linked libraries.
Static Linking
Layer

Library

Compatibility

Fortran interface layer

mkl_blas95.lib

Not applicable1

mkl_lapack95.lib
Interface layer

mkl_intel_c.lib (cdecl)✓✓

Threading layer

Computational layer

mkl_sequential.lib✓

mkl_tbb_thread.lib✓ (if using UWD Intel® TBB 2)

mkl_intel_thread.lib(OpenMP)╳

mkl_core.lib✓

1 These are Fortran 95 wrappers for BLAS (BLAS95) and LAPACK (LAPACK95). Windows drivers are typically

written in C, C++ or ASM. Fortran wrappers are UWD-compliant but not useful for building UWDs.
2 See Using Intel®TBB in UWP Applications. Starting with the Intel® TBB 2018 release, you can find a prebuilt

UWD-compliant Intel® TBB library in <tbb_distribution_root>\lib\<target_architecture>\vc14_uwd.

40Linking Your Application with the Intel® oneAPI Math Kernel Library3

Dynamic Linking
Layer

Library

Compatibility

Fortran interface layer

mkl_blas95_dll.lib

Not applicable1

mkl_lapack95_dll.lib
Interface layer

mkl_intel_c_dll.lib(cdecl)See UWD Compliance in thecompatibility table above.

Threading layer

mkl_sequential_dll.lib

See UWD Compliance in thecompatibility table above.

mkl_tbb_thread_dll.lib
mkl_intel_thread_dll.lib(OpenMP)
Computational layer

mkl_core_dll.lib

See UWD Compliance in thecompatibility table above.

1 These are Fortran 95 wrappers for BLAS (BLAS95) and LAPACK (LAPACK95). Windows drivers are typically

written in C, C++ or ASM. Fortran wrappers are UWD-compliant but not useful for building UWDs.

UWD Compliance for 64-bit Architecture (intel64)
These tables describe compliance between Intel® oneAPI Math Kernel Library and UWD on 64-bit (intel64)
architecture for static and dynamic linked libraries.
Static Linking
Layer

Library

Compatibility

Fortran interface layer

mkl_blas95_lp64.lib

Not applicable1

mkl_blas95_ilp64.lib
mkl_lapack95_lp64.lib
mkl_lapack95_ilp64.lib
Interface layer

Threading layer

mkl_intel_lp64.lib✓

mkl_intel_ilp64.lib✓

mkl_sequential.lib✓

mkl_tbb_thread.lib✓ (if using UWD Intel® TBB2)

mkl_intel_thread.lib(OpenMP)╳╳mkl_pgi_thread.lib (PGI
OpenMP)
Computational layer

mkl_core.lib

MPI layer

mkl_blacs_lp64.lib
mkl_blacs_ilp64.lib✓╳

mkl_cdft_core.lib
mkl_scalapack_lp64.lib

41

3Intel® oneAPI Math Kernel Libraryfor Windows* Developer Guide

Layer

Library

Compatibility

mkl_scalapack_ilp64.lib
1 These are Fortran 95 wrappers for BLAS (BLAS95) and LAPACK (LAPACK95). Windows drivers are typically

written in C, C++ or ASM. Fortran wrappers are UWD-compliant but not useful for building UWDs.
2 See Using Intel®TBB in UWP Applications. Starting with the Intel® TBB 2018 release, you can find a prebuilt

UWD-compliant Intel® TBB library in <tbb_distribution_root>\lib\<target_architecture>\vc14_uwd.
Dynamic Linking
Layer

Library

Compatibility

Fortran interface layer

mkl_blas95_lp64.lib

Not applicable1

mkl_blas95_ilp64.lib
mkl_lapack95_lp64.lib
mkl_lapack95_ilp64.lib
Interface layer

mkl_intel_lp64_dll.lib
mkl_intel_ilp64_dll.lib

Threading layer

mkl_sequential_dll.lib
mkl_tbb_thread_dll.lib

See UWD Compliance in thecompatibility table above.

See UWD Compliance in thecompatibility table above.

mkl_intel_thread_dll.lib(OpenMP)mkl_pgi_thread_dll.lib(PGI OpenMP)
Computational layer

mkl_core_dll.lib

MPI layer

mkl_blacs_lp64_dll.lib
mkl_blacs_ilp64_dll.lib
mkl_cdft_core_dll.lib

See UWD Compliance in thecompatibility table above.

See UWD Compliance in thecompatibility table above.

mkl_scalapack_lp64_dll.libmkl_scalapack_ilp64_dll.l
ib
1 These are Fortran 95 wrappers for BLAS (BLAS95) and LAPACK (LAPACK95). Windows drivers are typically

written in C, C++ or ASM. Fortran wrappers are UWD-compliant but not useful for building UWDs.

42

Managing Performance and Memory

Managing Performance andMemory4

4Product and Performance Information
Performance varies by use, configuration and other factors. Learn more at www.Intel.com/
PerformanceIndex.
Notice revision #20201201

Improving Performance with ThreadingIntel® oneAPI Math Kernel Library (oneMKL) is extensively parallelized. SeeOpenMP* Threaded Functions andProblems and Functions Threaded with Intel® Threading Building Blocks for lists of threaded functions andproblems that can be threaded.
Intel® oneAPI Math Kernel Library isthread-safe, which means that all Intel® oneAPI Math Kernel Libraryfunctions (except the LAPACK deprecated routine?lacon)work correctly during simultaneous execution bymultiple threads. In particular, any chunk of threaded Intel® oneAPI Math Kernel Library code provides accessfor multiple threads to the same shared data, while permitting only one thread at any given time to access ashared piece of data. Therefore, you can call Intel® oneAPI Math Kernel Library from multiple threads and notworry about the function instances interfering with each other.
If you are using OpenMP* threading technology, you can use the environment variable OMP_NUM_THREADSto
specify the number of threads or the equivalent OpenMP run-time function calls. Intel® oneAPI Math Kernel
Library also offers variables that are independent of OpenMP, such asMKL_NUM_THREADS, and equivalentIntel® oneAPI Math Kernel Library functions for thread management. The Intel® oneAPI Math Kernel Libraryvariables are always inspected first, then the OpenMP variables are examined, and if neither is used, theOpenMP software chooses the default number of threads.
By default, Intel® oneAPI Math Kernel Library uses the number ofOpenMP threads equal to the number ofphysical cores on the system.
If you are using the Intel TBB threading technology, the OpenMP threading controls, such as the
OMP_NUM_THREADS environment variable or MKL_NUM_THREADS function, have no effect. Use the Intel TBB
application programming interface to control the number of threads.
To achieve higher performance, set the number of threads to the number of processors or physical cores, assummarized in Techniques to Set the Number of Threads.

See Also
Managing Multi-core Performance

OpenMP* Threaded Functions and Problems
The following Intel® oneAPI Math Kernel Library function domains are threaded with the OpenMP*technology:••

Direct sparse solver.
LAPACK.

•For a list of threaded routines, see LAPACK Routines.
Level1 and Level2 BLAS.

•••

For a list of threaded routines, see BLAS Level1 and Level2 Routines.
All Level 3 BLAS and all Sparse BLAS routines except Level 2 Sparse Triangular solvers.
All Vector Mathematics functions (except service functions).
FFT.

43

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Intel® oneAPI Math Kernel Libraryfor Windows* Developer Guide

For a list of FFT transforms that can be threaded, see Threaded FFT Problems.

LAPACK Routines
In this section, ? stands for a precision prefix of each flavor of the respective routine and may have the valueof s, d, c, or z.
The following LAPACK routines are threaded with OpenMP*:•

Linear equations, computational routines:•

Factorization: ?getrf, ?getrfnpi, ?gbtrf, ?potrf, ?pptrf, ?sytrf, ?hetrf, ?sptrf, ?

hptrf
•
•

Solving: ?dttrsb, ?gbtrs, ?gttrs, ?pptrs, ?pbtrs, ?pttrs, ?sytrs, ?sptrs, ?hptrs, ?
tptrs, ?tbtrs

Orthogonal factorization, computational routines:?geqrf, ?ormqr, ?unmqr, ?ormlq, ?unmlq, ?ormql, ?unmql, ?ormrq, ?unmrq•

Singular Value Decomposition, computational routines:•

Symmetric Eigenvalue Problems, computational routines:?gebrd, ?bdsqr?sytrd, ?hetrd, ?sptrd, ?hptrd, ?steqr, ?stedc.
•

Generalized Nonsymmetric Eigenvalue Problems, computational routines:
chgeqz/zhgeqz.

A number of other LAPACK routines, which are based on threaded LAPACK or BLAS routines, make effective
use of OpenMP* parallelism:?gesv, ?posv, ?gels, ?gesvd, ?syev, ?heev, cgegs/zgegs, cgegv/zgegv, cgges/zgges,
cggesx/zggesx, cggev/zggev, cggevx/zggevx, and so on.

Threaded BLAS Level1 and Level2 Routines
In the following list, ? stands for a precision prefix of each flavor of the respective routine and may have thevalue of s, d, c, or z.
The following routines are threaded with OpenMP*:•

Level1 BLAS:?axpy, ?copy, ?swap, ddot/sdot, cdotc, drot/srot•

Level2 BLAS:?gemv, ?trsv, ?trmv, dsyr/ssyr, dsyr2/ssyr2, dsymv/ssymv

Threaded FFT Problems
The following characteristics of a specific problem determine whether your FFT computation may be threadedwith OpenMP*:
•
•
•
•
•
•
•
•

rank
domainsize/length
precision (single or double)
placement (in-place or out-of-place)
stridesnumber of transforms
layout (for example, interleaved or split layout of complex data)

Most FFT problems are threaded. In particular, computation of multiple transforms in one call (number oftransforms > 1) is threaded. Details of which transforms are threaded follow.
One-dimensional (1D) transforms

44

Managing Performance and Memory

4

1D transforms are threaded in many cases.
1D complex-to-complex (c2c) transforms of size N using interleaved complex data layout are threaded under
the following conditions depending on the architecture:
Architecture

Conditions

Intel® 64

N is a power of 2, log2(N) > 9, the transform is double-precision out-of-place, andinput/output strides equal 1.

IA-32

N is a power of 2, log2(N) > 13, and the transform is single-precision.
N is a power of 2, log2(N) > 14, and the transform is double-precision.

Any

N is composite, log2(N) > 16, and input/output strides equal 1.

1D complex-to-complex transforms using split-complex layout are not threaded.
Multidimensional transforms
All multidimensional transforms on large-volume data are threaded.

Functions Threaded with Intel® Threading Building Blocks
In this section, ? stands for a precision prefix or suffix of the routine name and may have the value of s, d,c, or z.
The following Intel® oneAPI Math Kernel Library function domains are threaded with Intel® Threading Building
Blocks (Intel® TBB):•LAPACK.

•••••For a list of threaded routines, see LAPACK Routines.
Entire Level3 BLAS.
Level2 BLAS – ?GEMV.
Fast Poisson, Laplace, and Helmholtz Solver (Poisson Library).
All Vector Mathematics functions (except service functions).
Intel® oneAPI Math Kernel Library PARDISO, a direct sparse solver based on Parallel Direct Sparse Solver(PARDISO*).

•

For details, see oneMKL PARDISO Steps.
Sparse BLAS.
For a list of threaded routines, see Sparse BLAS Routines.

LAPACK Routines
The following LAPACK routines are threaded with Intel TBB:

?geqrf, ?gelqf, ?getrf, ?potrf, ?unmqr*, ?ormqr*, ?unmrq*, ?ormrq*, ?unmlq*, ?ormlq*, ?unmql*,?ormql*, ?sytrd, ?hetrd, ?syev, ?heev, and ?latrd.
A number of other LAPACK routines, which are based on threaded LAPACK or BLAS routines, make effective
use of Intel TBB threading:?getrs, ?gesv, ?potrs, ?bdsqr, and ?gels.

oneMKL PARDISO StepsIntel® oneAPI Math Kernel Library PARDISO is threaded with Intel TBB in the reordering and factorization
steps. However, routines performing the solving step are still called sequentially when using Intel TBB.

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Sparse BLAS Routines
The Sparse BLAS inspector-executor application programming interface routines mkl_sparse_?_mv arethreaded with Intel TBB for the general compressed sparse row (CSR) and block sparse row (BSR) formats.
The following Sparse BLAS inspector-executor application programming routines are threaded with Intel TBB:•

mkl_sparse_?_mv using the general compressed sparse row (CSR) and block sparse row (BSR) matrix•

mkl_sparse_?_mm using the general CSR sparse matrix format and both row and column major storage

formats.
formats for the dense matrix.

Avoiding Conflicts in the Execution Environment
Certain situations can cause conflicts in the execution environment that make the use of threads in Intel®
oneAPI Math Kernel Library problematic. This section briefly discusses why these problems exist and how to
avoid them.
If your program is parallelized by other means than Intel® OpenMP* run-time library (RTL) and Intel TBB
RTL, several calls to Intel® oneAPI Math Kernel Library may operate in a multithreaded mode at the sametime and result in slow performance due to overuse of machine resources.
The following table considers several cases where the conflicts may arise and provides recommendations
depending on your threading model:
Threading model

Discussion

You parallelize the programusing the technology otherthan Intel OpenMP and IntelTBB (for example: Win32*
threads on Windows*).

If more than one thread calls Intel® oneAPI Math Kernel Library, and thefunction being called is threaded, it may be important that you turn off
Intel® oneAPI Math Kernel Library threading. Set the number of threads
to one by any of the available means (seeTechniques to Set the Numberof Threads).

You parallelize the programusing OpenMP directives
and/or pragmas and compile
the program using a non-Intel
compiler.

To avoid simultaneous activities of multiple threading RTLs, link the
program against the Intel® oneAPI Math Kernel Library threading librarythat matches the compiler you use (see Linking Exampleson how to dothis). If this is not possible, use Intel® oneAPI Math Kernel Library in thesequential mode. To do this, you should link with the appropriate
threading library:mkl_sequential.lib or mkl_sequential.dll (seeAppendix C: Directory Structure in Detail).

You thread the program using
Intel TBB threading
technology and compile theprogram using a non-Intel
compiler.

To avoid simultaneous activities of multiple threading RTLs, link the
program against the Intel® oneAPI Math Kernel Library Intel TBB
threading library and Intel TBB RTL if it matches the compiler you use. If
this is not possible, use Intel® oneAPI Math Kernel Library in thesequential mode. To do this, link with the appropriate threading
library:mkl_sequential.lib or mkl_sequential_dll.lib (seeAppendix C: Directory Structure in Detail).

You run multiple programs
calling Intel® oneAPI Math
Kernel Library on amultiprocessor system, forexample, a program
parallelized using a messagepassing interface (MPI).

The threading RTLs from different programs you run may place a large
number of threads on the same processor on the system and therefore
overuse the machine resources. In this case, one of the solutions is to set
the number of threads to one by any of the available means (seeTechniques to Set the Number of Threads). The Intel® Distribution for
LINPACK* Benchmark section discusses another solution for a Hybrid(OpenMP* + MPI) mode.

Using the mkl_set_num_threads and mkl_domain_set_num_threadsfunctions to control parallelism ofIntel® oneAPI Math Kernel Library from parallel user threads may result in a race condition that impacts theperformance of the application because these functions operate on internal control variables that are global,that is, apply to all threads. For example, if parallel user threads call these functions to set different numbers

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Managing Performance and Memory

4of threads for the same function domain, the number of threads actually set is unpredictable. To avoid thiskind of data races, use themkl_set_num_threads_local function (see the "Support Functions" chapter in
the Intel® oneAPI Math Kernel Library Developer Reference for the function description).

See Also
Using Additional Threading Control
Linking with Compiler Support RTLs

Techniques to Set the Number of Threads
Use the following techniques to specify the number of OpenMP threads to use in Intel® oneAPI Math Kernel
Library:•

Set one of the OpenMP or Intel® oneAPI Math Kernel Library environment variables:•• OMP_NUM_THREADS• MKL_NUM_THREADS• MKL_DOMAIN_NUM_THREADS
Call one of the OpenMP or Intel® oneAPI Math Kernel Library functions:••••

omp_set_num_threads()
mkl_set_num_threads()
mkl_domain_set_num_threads()
mkl_set_num_threads_local()
NOTE
A call to the mkl_set_num_threads or mkl_domain_set_num_threadsfunction changes the numberof OpenMP threads available to all in-progress calls (in concurrent threads) and future calls to Intel®
oneAPI Math Kernel Library and may result in slow Intel® oneAPI Math Kernel Library performance
and/or race conditions reported by run-time tools, such as Intel® Inspector.
To avoid such situations, use the mkl_set_num_threads_local function (see the "Support Functions"section in the Intel® oneAPI Math Kernel Library Developer Reference for the function description).

When choosing the appropriate technique, take into account the following rules:•••

The Intel® oneAPI Math Kernel Library threading controls take precedence over the OpenMP controls
because they are inspected first.
A function call takes precedence over any environment settings. The exception, which is a consequence ofthe previous rule, is that a call to the OpenMP subroutine omp_set_num_threads()does not haveprecedence over the settings of Intel® oneAPI Math Kernel Library environment variables such
asMKL_NUM_THREADS. See Using Additional Threading Control for more details.
You cannot change run-time behavior in the course of the run using the environment variables because
they are read only once at the first call to Intel® oneAPI Math Kernel Library.

If you use the Intel TBB threading technology, read the documentation for the tbb::task_scheduler_initclass at https://www.threadingbuildingblocks.org/documentation to find out how to specify the number of
threads.

Setting the Number of Threads Using an OpenMP* Environment Variable
You can set the number of threads using the environment variable OMP_NUM_THREADS. To change the numberof OpenMP threads, in the command shell in which the program is going to run, enter:

set OMP_NUM_THREADS=<number of threads to use>.
Some shells require the variable and its value to be exported:

export OMP_NUM_THREADS=<number of threads to use>.
You can alternatively assign value to the environment variable using Microsoft Windows*OS Control Panel.

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Note that you will not benefit from setting this variable on Microsoft Windows* 98 orWindows* ME because
multiprocessing is not supported.

See Also
Using Additional Threading Control

Changing the Number of OpenMP* Threads at Run TimeYou cannot change the number of OpenMP threads at run time using environment variables. However, you
can call OpenMP routines to do this. Specifically, the following sample code shows how to change the numberof threads during run time using the omp_set_num_threads() routine. For more options, see also
Techniques to Set the Number of Threads.
The example is provided for both C and Fortran languages. To run the example in C, use the omp.h header
file from the Intel(R) compiler package. If you do not have the Intel compiler but wish to explore thefunctionality in the example, use Fortran API for omp_set_num_threads() rather than the C version. For
example, omp_set_num_threads_( &i_one );// ******* C language *******#include "omp.h"#include "mkl.h"#include <stdio.h>#define SIZE 1000
int main(int args, char *argv[]){double *a, *b, *c;a = (double*)malloc(sizeof(double)*SIZE*SIZE);b = (double*)malloc(sizeof(double)*SIZE*SIZE);
c = (double*)malloc(sizeof(double)*SIZE*SIZE);
double alpha=1, beta=1;
int m=SIZE, n=SIZE, k=SIZE, lda=SIZE, ldb=SIZE, ldc=SIZE, i=0, j=0;char transa='n', transb='n';for( i=0; i<SIZE; i++){for( j=0; j<SIZE; j++){a[i*SIZE+j]= (double)(i+j);b[i*SIZE+j]= (double)(i*j);c[i*SIZE+j]= (double)0;}}
cblas_dgemm(CblasRowMajor, CblasNoTrans, CblasNoTrans,
m, n, k, alpha, a, lda, b, ldb, beta, c, ldc);
printf("row\ta\tc\n");for ( i=0;i<10;i++){printf("%d:\t%f\t%f\n", i, a[i*SIZE], c[i*SIZE]);}
omp_set_num_threads(1);for( i=0; i<SIZE; i++){for( j=0; j<SIZE; j++){a[i*SIZE+j]= (double)(i+j);b[i*SIZE+j]= (double)(i*j);c[i*SIZE+j]= (double)0;}}
cblas_dgemm(CblasRowMajor, CblasNoTrans, CblasNoTrans,48

Managing Performance and Memory

4m, n, k, alpha, a, lda, b, ldb, beta, c, ldc);
printf("row\ta\tc\n");for ( i=0;i<10;i++){printf("%d:\t%f\t%f\n", i, a[i*SIZE], c[i*SIZE]);}
omp_set_num_threads(2);for( i=0; i<SIZE; i++){for( j=0; j<SIZE; j++){a[i*SIZE+j]= (double)(i+j);b[i*SIZE+j]= (double)(i*j);c[i*SIZE+j]= (double)0;}}
cblas_dgemm(CblasRowMajor, CblasNoTrans, CblasNoTrans,
m, n, k, alpha, a, lda, b, ldb, beta, c, ldc);
printf("row\ta\tc\n");for ( i=0;i<10;i++){printf("%d:\t%f\t%f\n", i, a[i*SIZE],
c[i*SIZE]);}
free (a);free (b);free (c);return 0;}// ******* Fortran language *******PROGRAM DGEMM_DIFF_THREADSINTEGER N, I, J
PARAMETER (N=100)
REAL*8 A(N,N),B(N,N),C(N,N)
REAL*8 ALPHA, BETA
ALPHA = 1.1
BETA = -1.2
DO I=1,N
DO J=1,N
A(I,J) = I+J
B(I,J) = I*j
C(I,J) = 0.0
END DOEND DOCALL DGEMM('N','N',N,N,N,ALPHA,A,N,B,N,BETA,C,N)
print *,'Row A C'
DO i=1,10
write(*,'(I4,F20.8,F20.8)') I, A(1,I),C(1,I)
END DOCALL OMP_SET_NUM_THREADS(1);DO I=1,N
DO J=1,N
A(I,J) = I+J
B(I,J) = I*j49

4Intel® oneAPI Math Kernel Libraryfor Windows* Developer Guide

C(I,J) = 0.0
END DOEND DOCALL DGEMM('N','N',N,N,N,ALPHA,A,N,B,N,BETA,C,N)
print *,'Row A C'
DO i=1,10
write(*,'(I4,F20.8,F20.8)') I, A(1,I),C(1,I)
END DOCALL OMP_SET_NUM_THREADS(2);DO I=1,N
DO J=1,N
A(I,J) = I+J
B(I,J) = I*j
C(I,J) = 0.0
END DOEND DOCALL DGEMM('N','N',N,N,N,ALPHA,A,N,B,N,BETA,C,N)
print *,'Row A C'
DO i=1,10
write(*,'(I4,F20.8,F20.8)') I, A(1,I),C(1,I)
END DO
STOP
END

Using Additional Threading Control
oneMKL-specific Environment Variables for OpenMP Threading Control
Intel® oneAPI Math Kernel Library provides environment variables and support functions to control Intel®
oneAPI Math Kernel Library threading independently of OpenMP. The Intel® oneAPI Math Kernel Libraryspecific threading controls take precedence over their OpenMP equivalents. Use the Intel® oneAPI Math
Kernel Library-specific threading controls to distribute OpenMP threads between Intel® oneAPI Math Kernel
Library and the rest of your program.
NOTE
Some Intel® oneAPI Math Kernel Library routines may use fewer OpenMP threads than suggested by
the threading controls if either the underlying algorithms do not support the suggested number of
OpenMP threads or the routines perform better with fewer OpenMP threads because of lower OpenMP
overhead and/or better data locality. Set theMKL_DYNAMIC environment variable to FALSE or call
mkl_set_dynamic(0) to use the suggested number of OpenMP threads whenever the algorithms
permit and regardless of OpenMP overhead and data locality.

Section "Number of User Threads" in the "Fourier Transform Functions" chapter of the Intel® oneAPI Math
Kernel Library Developer Referenceshows how the Intel® oneAPI Math Kernel Library threading controls help
to set the number of threads for the FFT computation.
The table below lists the Intel® oneAPI Math Kernel Library environment variables for threading control, theirequivalent functions, and OMP counterparts:
Environment Variable

Support Function

Comment

Equivalent OpenMP*
Environment Variable

MKL_NUM_THREADS

mkl_set_num_threads

Suggests the number of
OpenMP threads to use.

OMP_NUM_THREADS

50

Managing Performance and Memory

Environment Variable

Support Function

Comment

4

Equivalent OpenMP*
Environment Variable

mkl_set_num_threads_localMKL_DOMAIN_NUM_
THREADS

mkl_domain_set_num_
threads

Suggests the number of
OpenMP threads for aparticular function
domain.

MKL_DYNAMIC

mkl_set_dynamic

Enables Intel® oneAPI
Math Kernel Library todynamically change thenumber of OpenMP
threads.

OMP_DYNAMIC

NOTE
Call mkl_set_num_threads()to force Intel® oneAPI Math Kernel Library to use a given number of
OpenMP threads and prevent it from reacting to the environment variablesMKL_NUM_THREADS,MKL_DOMAIN_NUM_THREADS, and OMP_NUM_THREADS.

The example below shows how to force Intel® oneAPI Math Kernel Library to use one thread:// ******* C language *******#include <mkl.h>...
mkl_set_num_threads ( 1 );// ******* Fortran language *******...
call mkl_set_num_threads( 1 )See the Intel® oneAPI Math Kernel Library Developer Reference for the detailed description of the threading
control functions, their parameters, calling syntax, and more code examples.

MKL_DYNAMIC
The MKL_DYNAMICenvironment variable enables Intel® oneAPI Math Kernel Library to dynamically change thenumber of threads.
The default value of MKL_DYNAMIC is TRUE, regardless of OMP_DYNAMIC, whose default value may be FALSE.
When MKL_DYNAMIC is TRUE,Intel® oneAPI Math Kernel Library may use fewer OpenMP threads than the
maximum number you specify.
For example, MKL_DYNAMIC set to TRUE enables optimal choice of the number of threads in the following
cases:•

If the requested number of threads exceeds the number of physical cores (perhaps because of using theIntel® Hyper-Threading Technology), Intel® oneAPI Math Kernel Library scales down the number of
OpenMP threads to the number of physical cores.

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•

Intel® oneAPI Math Kernel Libraryfor Windows* Developer Guide

If you are able to detect the presence of a message-passing interface (MPI), but cannot determine
whether it has been called in a thread-safe mode, Intel® oneAPI Math Kernel Library runs one OpenMP
thread.

When MKL_DYNAMIC is FALSE, Intel® oneAPI Math Kernel Library uses the suggested number of OpenMP
threads whenever the underlying algorithms permit.For example, if you attempt to do a size one matrixmatrix multiply across eight threads, the library may instead choose to use only one thread because it isimpractical to use eight threads in this event.
If Intel® oneAPI Math Kernel Library is called from an OpenMP parallel region in your program, Intel® oneAPI
Math Kernel Library uses only one thread by default. If you want Intel® oneAPI Math Kernel Library to go
parallel in such a call, link your program against an OpenMP threading RTL supported by Intel® oneAPI Math
Kernel Library and set the environment variables:•••

OMP_NESTED to TRUE
OMP_DYNAMIC and MKL_DYNAMIC to FALSE
MKL_NUM_THREADS to some reasonable value

With these settings, Intel® oneAPI Math Kernel Library usesMKL_NUM_THREADS threads when it is called fromthe OpenMP parallel region in your program.
In general, set MKL_DYNAMIC to FALSEonly under circumstances that Intel® oneAPI Math Kernel Library is
unable to detect, for example, to use nested parallelism where the library is already called from a parallel
section.

MKL_DOMAIN_NUM_THREADS
The MKL_DOMAIN_NUM_THREADS environment variable suggests the number of OpenMP threads for aparticular function domain.

MKL_DOMAIN_NUM_THREADS accepts a string value <MKL-env-string>, which must have the following

format:<MKL-env-string> ::= <MKL-domain-env-string> { <delimiter><MKL-domain-env-string> }<delimiter> ::= [ <space-symbol>* ] ( <space-symbol> | <comma-symbol> | <semicolonsymbol> | <colon-symbol>) [ <space-symbol>* ]<MKL-domain-env-string> ::= <MKL-domain-env-name><uses><number-of-threads><MKL-domain-env-name> ::= MKL_DOMAIN_ALL | MKL_DOMAIN_BLAS | MKL_DOMAIN_FFT |MKL_DOMAIN_VML | MKL_DOMAIN_PARDISO<uses> ::= [ <space-symbol>* ] ( <space-symbol> | <equality-sign> | <comma-symbol>)[ <space-symbol>* ]<number-of-threads> ::= <positive-number><positive-number> ::= <decimal-positive-number> | <octal-number> | <hexadecimal-number>In the syntax above, values of <MKL-domain-env-name> indicate function domains as follows:

MKL_DOMAIN_ALL

All function domains

MKL_DOMAIN_BLAS

BLAS Routines

MKL_DOMAIN_FFT

non-cluster Fourier Transform Functions

MKL_DOMAIN_LAPACKLAPACK Routines

MKL_DOMAIN_VML

Vector Mathematics (VM)

MKL_DOMAIN_PARDISOIntel® oneAPI Math Kernel Library PARDISO, a direct sparse solver
based on Parallel Direct Sparse Solver (PARDISO*)For example, you could set the MKL_DOMAIN_NUM_THREADSenvironment variable to any of the following string
variants, in this case, defining three specific domain variables internal to Intel® oneAPI Math Kernel Library:52

Managing Performance and Memory

4

MKL_DOMAIN_NUM_THREADS="MKL_DOMAIN_ALL=2, MKL_DOMAIN_BLAS=1, MKL_DOMAIN_FFT=4"
MKL_DOMAIN_NUM_THREADS="MKL_DOMAIN_ALL 2 : MKL_DOMAIN_BLAS 1 : MKL_DOMAIN_FFT 4"
MKL_DOMAIN_NUM_THREADS="MKL_DOMAIN_ALL=2 : MKL_DOMAIN_BLAS=1 : MKL_DOMAIN_FFT=4"
MKL_DOMAIN_NUM_THREADS="MKL_DOMAIN_ALL=2; MKL_DOMAIN_BLAS=1; MKL_DOMAIN_FFT=4"
MKL_DOMAIN_NUM_THREADS="MKL_DOMAIN_ALL=2 MKL_DOMAIN_BLAS 1, MKL_DOMAIN_FFT 4"
MKL_DOMAIN_NUM_THREADS="MKL_DOMAIN_ALL,2: MKL_DOMAIN_BLAS 1, MKL_DOMAIN_FFT,4"
NOTE Prepend the appropriate set/export/setenv command for your command shell and operating
system. Refer to Setting the Environment Variables for Threading Control for more details.
The global variables MKL_DOMAIN_ALL, MKL_DOMAIN_BLAS, MKL_DOMAIN_FFT, MKL_DOMAIN_VML, and
MKL_DOMAIN_PARDISO, as well as the interface for the Intel® oneAPI Math Kernel Library threading control
functions, can be found in themkl.h header file.
NOTE You can retrieve the values of the specific domain variables that you have set in your code witha call to the mkl_get_domain_max_threads(domain_name) function per the Fortran and C interface
with the desired domain variable name.
This table illustrates how values of MKL_DOMAIN_NUM_THREADS are interpreted.
Value ofInterpretation

MKL_DOMAIN_NUM_
THREADS
MKL_DOMAIN_ALL=4

All parts of Intel® oneAPI Math Kernel Library should try four OpenMP threads. The
actual number of threads may be still different because of theMKL_DYNAMIC setting
or system resource issues. The setting is equivalent to MKL_NUM_THREADS = 4.

MKL_DOMAIN_ALL=
1,
MKL_DOMAIN_BLAS=4

All parts of Intel® oneAPI Math Kernel Library should try one OpenMP thread, except
for BLAS, which is suggested to try four threads.

MKL_DOMAIN_VML=
2

VM should try two OpenMP threads. The setting affects no other part of Intel®
oneAPI Math Kernel Library.

Be aware that the domain-specific settings take precedence over the overall ones. For example, the"MKL_DOMAIN_BLAS=4" value of MKL_DOMAIN_NUM_THREADS suggests trying four OpenMP threads for BLAS,
regardless of later setting MKL_NUM_THREADS, and a function call "mkl_domain_set_num_threads ( 4,
MKL_DOMAIN_BLAS );" suggests the same, regardless of later calls to mkl_set_num_threads().
However, a function call with input "MKL_DOMAIN_ALL", such as "mkl_domain_set_num_threads (4,MKL_DOMAIN_ALL);" is equivalent to "mkl_set_num_threads(4)", and thus it will be overwritten by latercalls to mkl_set_num_threads. Similarly, the environment setting of MKL_DOMAIN_NUM_THREADS with
"MKL_DOMAIN_ALL=4" will be overwritten with MKL_NUM_THREADS = 2.
Whereas the MKL_DOMAIN_NUM_THREADS environment variable enables you set several variables at once, forexample, "MKL_DOMAIN_BLAS=4,MKL_DOMAIN_FFT=2", the corresponding function does not take string
syntax. So, to do the same with the function calls, you may need to make several calls, which in thisexample are as follows:

mkl_domain_set_num_threads ( 4, MKL_DOMAIN_BLAS );mkl_domain_set_num_threads ( 2, MKL_DOMAIN_FFT );53

4Intel® oneAPI Math Kernel Libraryfor Windows* Developer Guide

MKL_NUM_STRIPES
The MKL_NUM_STRIPESenvironment variable controls the Intel® oneAPI Math Kernel Library threading
algorithm for?gemm functions. When MKL_NUM_STRIPES is set to a positive integer value nstripes, Intel®
oneAPI Math Kernel Library tries to use a number of partitions equal tonstripes along the leading dimensionof the output matrix.
The following table explains how the value nstripes of MKL_NUM_STRIPESdefines the partitioning algorithm
used by Intel® oneAPI Math Kernel Library for?gemm output matrix; max_threads_for_mkldenotes the
maximum number of OpenMP threads for Intel® oneAPI Math Kernel Library:
Value of

Partitioning Algorithm

MKL_NUM_STRIPES
1 < nstripes <(max_threads_for_mkl/
2)••

2D partitioning with the number of partitions equal to nstripes:

nstripes = 1

1D partitioning algorithm along the opposite direction of the leading dimension.

nstripes ≥(max_threads_for_mkl /2)

1D partitioning algorithm along the leading dimension.

nstripes < 0

The default Intel® oneAPI Math Kernel Library threading algorithm.

Horizontal, for column-major ordering.
Vertical, for row-major ordering.

The following figure shows the partitioning of an output matrix for nstripes = 4 and a total number of 8
OpenMP threads for column-major and row-major orderings:

You can use support functions mkl_set_num_stripes and mkl_get_num_stripes to set and query the
number of stripes, respectively.

Setting the Environment Variables for Threading Control
To set the environment variables used for threading control, in the command shell in which the program isgoing to run, enter:set <VARIABLE NAME>=<value>54

Managing Performance and Memory4

For example:

set MKL_NUM_THREADS=4
set MKL_DOMAIN_NUM_THREADS="MKL_DOMAIN_ALL=1, MKL_DOMAIN_BLAS=4"
set MKL_DYNAMIC=FALSE
set MKL_NUM_STRIPES=4
Some shells require the variable and its value to be exported:

export <VARIABLE NAME>=<value>
For example:

export MKL_NUM_THREADS=4
export MKL_DOMAIN_NUM_THREADS="MKL_DOMAIN_ALL=1, MKL_DOMAIN_BLAS=4"
export MKL_DYNAMIC=FALSE
export MKL_NUM_STRIPES=4
You can alternatively assign values to the environment variables using Microsoft Windows* OS Control Panel.

Calling oneMKL Functions from Multi-threaded Applications
This section summarizes typical usage models and available options for calling Intel® oneAPI Math Kernel
Library functions from multi-threaded applications. These recommendations apply to any multi-threading
environments: OpenMP*, Intel® Threading Building Blocks,Windows* threads, and others.

Usage model: disable oneMKL internal threading for the whole application
When used:Intel® oneAPI Math Kernel Library internal threading interferes with application's own threadingor may slow down the application.
Example: the application is threaded at top level, or the application runs concurrently with otherapplications.
Options:••

Link statically or dynamically with the sequential library
Link with the Single Dynamic Library mkl_rt.lib and select the sequential library using an environment
variable or a function call:••

Set MKL_THREADING_LAYER=sequential
Call mkl_set_threading_layer(MKL_THREADING_SEQUENTIAL)‡

Usage model: partition system resources among application threads
When used: application threads are specialized for a particular computation.
Example: one thread solves equations on all cores but one, while another thread running on a single core
updates a database.
Linking Options:••

Link statically or dynamically with a threading library
Link with the Single Dynamic Library mkl_rt.lib and select a threading library using an environment
variable or a function call:••set MKL_THREADING_LAYER=intel or MKL_THREADING_LAYER=tbb
call mkl_set_threading_layer(MKL_THREADING_INTEL) or

mkl_set_threading_layer(MKL_THREADING_TBB)Other Options for OpenMP Threading:

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Set the MKL_NUM_THREADSenvironment variable to a desired number of OpenMP threads for Intel® oneAPI
Math Kernel Library.
Set the MKL_DOMAIN_NUM_THREADSenvironment variable to a desired number of OpenMP threads forIntel® oneAPI Math Kernel Library for a particular function domain.

•Use if the application threads work with different Intel® oneAPI Math Kernel Library function domains.
Call mkl_set_num_threads()•

Use to globally set a desired number of OpenMP threads for Intel® oneAPI Math Kernel Library at run time.
Call mkl_domain_set_num_threads().

•

Use if at some point application threads start working with different Intel® oneAPI Math Kernel Libraryfunction domains.
Call mkl_set_num_threads_local().
Use to set the number of OpenMP threads for Intel® oneAPI Math Kernel Library called from a particularthread.
NOTE
If your application uses OpenMP* threading, you may need to provide additional settings:••

Set the environment variable OMP_NESTED=TRUE, or alternatively call omp_set_nested(1), toenable OpenMP nested parallelism.
Set the environment variable MKL_DYNAMIC=FALSE, or alternatively call mkl_set_dynamic(0), toprevent Intel® oneAPI Math Kernel Library from dynamically reducing the number of OpenMP
threads in nested parallel regions.

‡ For details of the mentioned functions, see the Support Functions section of the Intel® oneAPI Math Kernel

Library Developer Reference, available in the Intel Software Documentation Library.
Product and Performance Information
Performance varies by use, configuration and other factors. Learn more at www.Intel.com/
PerformanceIndex.
Notice revision #20201201

See Also
Linking with Threading Libraries
Dynamically Selecting the Interface and Threading Layer
oneMKL-specific Environment Variables for OpenMP Threading Control
MKL_DOMAIN_NUM_THREADS

Avoiding Conflicts in the Execution EnvironmentIntel Software Documentation Library

Using Intel® Hyper-Threading Technology
Intel® Hyper-Threading Technology (Intel® HT Technology) is especially effective when each thread performs
different types of operations and when there are under-utilized resources on the processor. However, Intel®
oneAPI Math Kernel Library fits neither of these criteria because the threaded portions of the library executeat high efficiencies using most of the available resources and perform identical operations on each thread.
You may obtain higher performance by disabling Intel HT Technology.
If you run with Intel HT Technology enabled, performance may be especially impacted if you run on fewer
threads than physical cores. Moreover, if, for example, there are two threads to every physical core, thethread scheduler may assign two threads to some cores and ignore the other cores altogether. If you are56

Managing Performance and Memory

4using the OpenMP* library of the Intel Compiler, read the respective User Guide on how to best set thethread affinity interface to avoid this situation. For Intel® oneAPI Math Kernel Library, apply the following
setting:

export KMP_AFFINITY=granularity=fine,compact,1,0
If you are using the Intel TBB threading technology, read the documentation on the
tbb::affinity_partitioner class at https://www.threadingbuildingblocks.org/documentation to find out
how to affinitize Intel TBB threads.

Managing Multi-core Performance
You can obtain best performance on systems with multi-core processors by requiring thatthreads do not
migrate from core to core. To do this, bind threads to the CPU cores bysetting an affinity mask to threads.
Use one of the following options:•••

OpenMP facilities (if available), for example, theKMP_AFFINITYenvironment variable using the Intel
OpenMP library
A system function, as explained below
Intel TBB facilities (if available), for example, the tbb::affinity_partitioner class (for details, see
https://www.threadingbuildingblocks.org/documentation)

Consider the following performance issue:••

The system has two sockets with two cores each, for a total of four cores (CPUs).
The application sets the number of OpenMP threads to four and calls an Intel® oneAPI Math Kernel Library
LAPACK routine. This call takes considerably different amounts of time from run to run.

To resolve this issue, before calling Intel® oneAPI Math Kernel Library, set an affinity mask for each OpenMP
thread using theKMP_AFFINITY environment variable or the SetThreadAffinityMask system function.
The following code example shows how to resolve the issue by setting an affinity mask by operating systemmeans using the Intel compiler. The code calls the functionSetThreadAffinityMask to bind the threads
toappropriatecores,preventing migration of the threads. Then the Intel® oneAPI Math Kernel LibraryLAPACK
routineis called:// Set affinity mask#include <windows.h>#include <omp.h>
int main(void) {#pragma omp parallel default(shared){
int tid = omp_get_thread_num();// 2 packages x 2 cores/pkg x 1 threads/core (4 total cores)
DWORD_PTR mask = (1 << (tid == 0 ? 0 : 2 ));SetThreadAffinityMask( GetCurrentThread(), mask );}// Call Intel MKL LAPACK routine
return 0;}

Compile the application with the Intel compiler using the following command:

icl /Qopenmp test_application.c
wheretest_application.cis the filename for the application.
Build the application. Run it in four threads, for example, by using the environment variable to set the
number of threads:

set OMP_NUM_THREADS=4
test_application.exe

574Intel® oneAPI Math Kernel Libraryfor Windows* Developer Guide

See Windows API documentation at msdn.microsoft.com/ for the restrictions on the usage of Windows API
routines and particulars of the SetThreadAffinityMask function used in the above example.
See also a similar example at en.wikipedia.org/wiki/Affinity_mask.

Managing Performance with Heterogeneous Cores
A hybrid architecture offers heterogeneous CPU cores. For example, the 12th Gen Intel® Core™ processor(Alder Lake) contains two types of cores: Performance-cores (P-cores) and Efficient-cores (E-cores).
Achieving the best performance on a hybrid architecture is harder because load balancing withheterogeneous cores is more complicated. Therefore, for hybrid architectures like Alder Lake, we recommend
running threads on the P-cores only. This approach might not yield the best performance, but it is simple andpredictable.
To specify P-cores with OpenMP, users can use the environment variable KMP_HW_SUBSET. For a detailed
description of this environment variable, refer to the Intel® C++ Compiler Classic Developer Guide andReference. In the case of an Alder Lake processor with eight P-cores, either of the following two commands
can be used for restricting threads to run only on the P-cores:

set KMP_HW_SUBSET=8c:intel_core—or—

set KMP_HW_SUBSET=8c:eff1
Note that for higher performance, Intel® Hyper-Threading Technology on P-cores must be disabled. You canachieve this either by changing the BIOS setting or by using KMP_HW_SUBSET to specify P-cores and onethread-per-core with the following command:set KMP_HW_SUBSET=8c:intel_core,1t—or—

set KMP_HW_SUBSET=8c:eff1,1t
If the user decides to adopt the more difficult approach of running on both P-cores and E-cores to maximize
performance, there are a few aspects to take into consideration:••••

Static versus dynamic load balancing
Problem size
Number of P-cores and E-cores
OpenMP versus oneTBB

If there are similar or equal numbers of P-cores and E-cores and if both core types are used, using static loadbalancing for splitting the work items is likely to result in lower performance because E-cores will take longer
to complete the work items assigned to them. For large GEMMs and {S,D}GETRF routines, oneMKL has
implemented dynamic load balancing with OpenMP and will automatically select the best load balancing
scheme. For most cases with small or regular problem sizes, static load balancing on P-cores is likely to give
better performance. If the problem size is very large, the overhead of dynamic scheduling is small comparedto overall computation time and dynamic load balancing will make more efficient use of P-cores and E-cores.
If the number of P-cores is much smaller than the number of E-cores, running on all cores may outperform
limiting computations to only P-cores. Additional performance measurements would be needed to determine
the best strategy.
As an alternative to OpenMP, users can also try oneTBB, which might give better results for a given set of
supported operations.

Improving Performance for Small Size Problems
The overhead of calling an Intel® oneAPI Math Kernel Library function for small problem sizes can besignificant when the functionhas a large number of parameters or internally checks parameter errors. To
reduce the performance overhead for these small size problems, the Intel® oneAPI Math Kernel Librarydirect58

Managing Performance and Memory4

callfeature works in conjunction with the compiler to preprocess the calling parameters to supported Intel®
oneAPI Math Kernel Library functions and directly call or inline special optimized small-matrix kernels thatbypass error checking.For a list of functions supporting direct call, see Limitations of the Direct Call.
To activate the feature, do the following:••

Compile your C or Fortran code with the preprocessor macro depending on whether a threaded or
sequential mode of Intel® oneAPI Math Kernel Library is required by supplying the compiler option as
explained below:
Intel® oneAPI Math Kernel
Library Mode

Macro

Compiler Option

Threaded

MKL_DIRECT_CALL/DMKL_DIRECT_CALL

Sequential

MKL_DIRECT_CALL_SEQ/DMKL_DIRECT_CALL_SEQ

For Fortran applications:••

Enable preprocessor by using the /fpp option for Intel® Fortran Compiler.
Include the Intel® oneAPI Math Kernel Library Fortran include filemkl_direct_call.fi.

Intel® oneAPI Math Kernel Library skips error checking and intermediate function calls if the problem size issmall enough (for example: a call to a function that supports direct call, such asdgemm, with matrix ranks
smaller than 50).
Product and Performance Information
Performance varies by use, configuration and other factors. Learn more at www.Intel.com/
PerformanceIndex.
Notice revision #20201201

Using MKL_DIRECT_CALL in C Applications
The following examples of code and link lines show how to activate direct calls to Intel® oneAPI Math Kernel
Library kernels in C applications:•

Include the mkl.h header file:#include "mkl.h"
int main(void) {// Call Intel MKL DGEMM•

return 0;}

For multi-threaded Intel® oneAPI Math Kernel Library, compile withMKL_DIRECT_CALL preprocessor
macro:

icl /DMKL_DIRECT_CALL /Qstd=c99 your_application.c mkl_intel_lp64.lib mkl_core.lib
mkl_intel_thread.lib /Qopenmp -I%MKLROOT%/include• To use Intel® oneAPI Math Kernel Library in the sequential mode, compile withMKL_DIRECT_CALL_SEQ
preprocessor macro:

icl /DMKL_DIRECT_CALL_SEQ /Qstd=c99 your_application.c mkl_intel_lp64.lib mkl_core.lib
mkl_sequential.lib -I%MKLROOT%/include

Using MKL_DIRECT_CALL in Fortran Applications
The following examples of code and link lines show how to activate direct calls to Intel® oneAPI Math Kernel
Library kernels in Fortran applications:

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Include mkl_direct_call.fi, to be preprocessed by the Fortran compiler preprocessor#

include "mkl_direct_call.fi"
program
DGEMM_MAIN

....
*
....

Call Intel MKL DGEMM
call sub1()
stop 1end*

A subroutine that calls DGEMM
subroutine sub1
Call Intel MKL DGEMM

*
•

end

For multi-threaded Intel® oneAPI Math Kernel Library, compile with/fpp option for Intel Fortran compiler
and with MKL_DIRECT_CALL preprocessor macro:

ifort /DMKL_DIRECT_CALL /fpp your_application.f mkl_intel_lp64.lib mkl_core.lib
mkl_intel_thread.lib/Qopenmp -I%MKLROOT%/include• To use Intel® oneAPI Math Kernel Library in the sequential mode, compile with/fpp option for Intel Fortrancompiler (or with -Mpreprocess for PGI compilers) and with MKL_DIRECT_CALL_SEQ preprocessor
macro:

ifort /DMKL_DIRECT_CALL_SEQ /fpp your_application.f mkl_intel_lp64.lib mkl_core.lib
mkl_sequential.lib-I%MKLROOT%/include

Limitations of the Direct Call
Directly calling the Intel® oneAPI Math Kernel Library kernels has the following limitations:•

If the MKL_DIRECT_CALL or MKL_DIRECT_CALL_SEQmacro is used, Intel® oneAPI Math Kernel Library may
skip error checking.
Important
With a limited error checking, you are responsible for checking the correctness of function parameters
to avoid unsafe and incorrect code execution.

•

•
•
•

The feature is only available for the following functions:• BLAS: ?gemm, ?gemm3m, ?syrk, ?trsm, ?axpy, and ?dot
• LAPACK: ?getrf, ?getrs, ?getri, ?potrf, and ?geqrf. (available for C applications only)Intel® oneAPI Math Kernel Library Verbose mode, Conditional Numerical Reproducibility, and BLAS95
interfaces are not supported.
GNU* Fortran compilers are not supported.
For C applications, you must enable mixing declarations and user code by providing the /Qstd=c99 option
for Intel® compilers.

Other Tips and Techniques to Improve Performance
See Also
Managing Performance of the Cluster Fourier Transform Functions

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Managing Performance and Memory

4

Coding TechniquesThis section discusses coding techniques to improve performance on processors based on supported
architectures.
To improve performance, properly align arrays in your code. Additional conditions can improve performancefor specific function domains.

Data Alignment and Leading Dimensions
To improve performance of your application that calls Intel® oneAPI Math Kernel Library, align your arrays on
64-byte boundaries and ensure that the leading dimensions of the arrays are divisible by 64/element_size,
where element_size is the number of bytes for the matrix elements (4 for single-precision real, 8 for doubleprecision real and single-precision complex, and 16 for double-precision complex) . For more details, see
Example of Data Alignment.
For Intel® Xeon Phi™ processor x200 product family, codenamed Knights Landing, align your matrices on
4096-byte boundaries and set the leading dimension to the following integer expression:(((n * element_size + 511) / 512) * 512 + 64) /element_size,
where n is the matrix dimension along the leading dimension.

LAPACK Packed Routines
The routines with the names that contain the letters HP, OP, PP, SP, TP, UPin the matrix type andstorage position (the second and third letters respectively) operate on the matrices in the packed format (seeLAPACK "Routine Naming Conventions" sections in the Intel® oneAPI Math Kernel Library Developer
Reference). Their functionality is strictly equivalent to the functionality of the unpacked routines with thenames containing the lettersHE, OR, PO, SY, TR, UN in the same positions, but the performance issignificantly lower.
If the memory restriction is not too tight, use an unpacked routine for better performance. In this case, youneed to allocate N2/2 more memory than the memory required by a respective packed routine, where N is
the problem size (the number of equations).
For example, to speed up solving a symmetric eigenproblem with an expert driver, use the unpacked routine:

call dsyevx(jobz, range, uplo, n, a, lda, vl, vu, il, iu, abstol, m, w, z, ldz, work, lwork,
iwork, ifail, info)
where a is the dimension lda-by-n, which is at least N2 elements,
instead of the packed routine:

call dspevx(jobz, range, uplo, n, ap, vl, vu, il, iu, abstol, m, w, z, ldz, work, iwork, ifail,
info)
where ap is the dimension N*(N+1)/2.

See Also
Managing Performance of the Cluster Fourier Transform Functions

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Improving oneMKL Performance on Specific Processors
Dual-Core Intel® Xeon® Processor 5100 Series
To get the best performance with Intel® oneAPI Math Kernel Library on Dual-Core Intel® Xeon® processor
5100 series systems, enable the Hardware DPL (streaming data) Prefetcher functionality of this processor. To
configure this functionality, use the appropriate BIOS settings, as described in your BIOS documentation.

Operating on Denormals
The IEEE 754-2008 standard, "An IEEE Standard for Binary Floating-Point Arithmetic", defines denormal (orsubnormal) numbers as non-zero numbers smaller than the smallest possible normalized numbers for a
specific floating-point format. Floating-point operations on denormals are slower than on normalized
operands because denormal operands and results are usually handled through a software assist mechanism
rather than directly in hardware. This software processing causes Intel® oneAPI Math Kernel Library functions
that consume denormals to run slower than with normalized floating-point numbers.
You can mitigate this performance issue by setting the appropriate bit fields in the MXCSR floating-point
control register to flush denormals to zero (FTZ) or to replace any denormals loaded from memory with zero
(DAZ). Check your compiler documentation to determine whether it has options to control FTZ and DAZ.
Note that these compiler options may slightly affect accuracy.

Using Memory Functions
Avoiding Memory Leaks in oneMKLWhen running, Intel® oneAPI Math Kernel Library (oneMKL) may allocate and deallocate internal buffers tofacilitate better performance. Memory leaks can occur if the Intel® oneAPI Math Kernel Library is unloaded
before freeing the internal buffers.
You can free the internal buffers by calling the mkl_free_buffers() function or, for more granular control,
the mkl_thread_free_buffers() function.
Alternatively, setting the MKL_DISABLE_FAST_MM environment variable to 1 or calling themkl_disable_fast_mm() function disables the internal memory manager. Be aware that this change may
negatively impact the performance of some oneMKL functions, especially for small problem sizes.

See Also
Intel Software Documentation LibraryRedefining Memory Functions
In C/C++ programs, you can replace Intel® oneAPI Math Kernel Library memory functions that the libraryuses by default with your own functions. To do this, use thememory renaming feature.

Memory Renaming
Intel® oneAPI Math Kernel Library memory management by default uses standard C run-time memory
functions to allocate or free memory. These functions can be replaced using memory renaming.
Intel® oneAPI Math Kernel Library accesses the memory functions by pointersi_malloc, i_free,
i_calloc, and i_realloc, which are visible at the application level. These pointers initially hold addressesof the standard C run-time memory functions malloc, free, calloc, and realloc, respectively. You canprogrammatically redefine values of these pointers to the addresses of your application's memory
management functions.

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Managing Performance and Memory

4

Redirecting the pointers is the only correct way to use your own set of memory management functions. If
you call your own memory functions without redirecting the pointers, the memory will get managed by two
independent memory management packages, which may cause unexpected memory issues.

How to Redefine Memory Functions
To redefine memory functions, use the following procedure:
If you areusing the statically linked Intel® oneAPI Math Kernel Library,1.

2.

Include the i_malloc.h header file in your code.
This header file contains all declarations required for replacing the memory allocation functions. The
header file also describes how memory allocation can be replaced in those Intel libraries that support
this feature.
Redefine values of pointers i_malloc, i_free, i_calloc, and i_reallocprior to the first call toIntel® oneAPI Math Kernel Library functions, as shown in the following example:#include "i_malloc.h". . .
i_malloc = my_malloc;
i_calloc = my_calloc;
i_realloc = my_realloc;
i_free= my_free;. . .
// Now you may call Intel MKL functions
If you are using the dynamically linked Intel® oneAPI Math Kernel Library,1.
2.

Include the i_malloc.h header file in your code.
Redefine values of pointers i_malloc_dll, i_free_dll, i_calloc_dll, and i_realloc_dllprior
to the first call to Intel® oneAPI Math Kernel Library functions, as shown in the following example:#include "i_malloc.h". . .
i_malloc_dll = my_malloc;
i_calloc_dll = my_calloc;
i_realloc_dll = my_realloc;
i_free_dll= my_free;. . .
// Now you may call Intel MKL functions

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Language-specific Usage
Options5

The Intel® oneAPI Math Kernel Library (oneMKL) provides broad support for Fortran and C/C++
programming. However, not all functions support both Fortran and C interfaces. For example, some LAPACK
functions have no C interface. You can call such functions from C using mixed-language programming.
If you want to use LAPACK or BLAS functions that support Fortran 77 in the Fortran 95 environment,
additional effort may be initially required to build compiler-specific interface libraries and modules from thesource code provided with Intel® oneAPI Math Kernel Library.
Product and Performance Information
Performance varies by use, configuration and other factors. Learn more at www.Intel.com/
PerformanceIndex.
Notice revision #20201201

See Also
Language Interfaces Support, by Function Domain

Using Language-Specific Interfaces with Intel® oneAPI Math
Kernel LibraryThis section discusses mixed-language programming and the use of language-specific interfaces with Intel®
oneAPI Math Kernel Library.
See also the "FFTW Interface to Intel® oneAPI Math Kernel Library" Appendix in the Intel® oneAPI Math Kernel
Library Developer Reference for details of the FFTW interfaces to Intel® oneAPI Math Kernel Library.

Interface Libraries and Modules
You can create the following interface libraries and modules using the respective makefiles located in theinterfaces directory.
File nameContains

Libraries, in Intel® oneAPI Math Kernel Library architecture-specific directories

mkl_blas95.lib1

Fortran 95 wrappers for BLAS (BLAS95) for IA-32 architecture.

mkl_blas95_ilp64.lib1

Fortran 95 wrappers for BLAS (BLAS95) supporting LP64
interface.

mkl_blas95_lp64.lib1

Fortran 95 wrappers for BLAS (BLAS95) supporting ILP64
interface.

mkl_lapack95.lib1

Fortran 95 wrappers for LAPACK (LAPACK95) for IA-32
architecture.

mkl_lapack95_lp64.lib1

Fortran 95 wrappers for LAPACK (LAPACK95) supporting LP64
interface.

mkl_lapack95_ilp64.lib1

Fortran 95 wrappers for LAPACK (LAPACK95) supporting ILP64
interface.

64

Language-specific Usage Options

5

File nameContains

fftw2xc_intel.lib1

Interfaces for FFTW version 2.x (C interface for Intel
compilers) to call Intel® oneAPI Math Kernel Library FFT.

fftw2xc_ms.lib

Contains interfaces for FFTW version 2.x (C interface forMicrosoft compilers) to call Intel® oneAPI Math Kernel LibraryFFT.

fftw2xf_intel.lib

Interfaces for FFTW version 2.x (Fortran interface for Intel
compilers) to call Intel® oneAPI Math Kernel Library FFT.

fftw3xc_intel.lib2

Interfaces for FFTW version 3.x (C interface for Intel compiler)
to call Intel® oneAPI Math Kernel Library FFT.

fftw3xc_ms.lib

Interfaces for FFTW version 3.x (C interface for Microsoft
compilers) to call Intel® oneAPI Math Kernel Library FFT.

fftw3xf_intel.lib2

Interfaces for FFTW version 3.x (Fortran interface for Intel
compilers) to call Intel® oneAPI Math Kernel Library FFT.

fftw2x_cdft_SINGLE.lib

Single-precision interfaces for MPI FFTW version 2.x (Cinterface) to call Intel® oneAPI Math Kernel Library cluster FFT.

fftw2x_cdft_DOUBLE.lib

Double-precision interfaces for MPI FFTW version 2.x (Cinterface) to call Intel® oneAPI Math Kernel Library cluster FFT.

fftw3x_cdft.lib

Interfaces for MPI FFTW version 3.x (C interface) to call Intel®
oneAPI Math Kernel Library cluster FFT.

fftw3x_cdft_ilp64.lib

Interfaces for MPI FFTW version 3.x (C interface) to call Intel®
oneAPI Math Kernel Library cluster FFT supporting the ILP64
interface.

Modules, in architecture- and interface-specific subdirectories of the Intel® oneAPI Math Kernel
Library include directoryblas95.mod1

Fortran 95 interface module for BLAS (BLAS95).

lapack95.mod1

Fortran 95 interface module for LAPACK (LAPACK95).

f95_precision.mod1

Fortran 95 definition of precision parameters for BLAS95 and
LAPACK95.

mkl_service.mod1

Fortran 95 interface module for Intel® oneAPI Math Kernel
Library support functions.

1 Prebuilt for the Intel® Fortran compiler
2FFTW3 interfaces are integrated with Intel® oneAPI Math Kernel Library. Look into<mkl

directory>\interfaces\fftw3x*\makefile for options defining how to build and where to place the standalone

library with the wrappers.

See Also
Fortran 95 Interfaces to LAPACK and BLAS

Fortran 95 Interfaces to LAPACK and BLAS
Fortran 95 interfaces are compiler-dependent. Intel® oneAPI Math Kernel Library provides the interface
libraries and modules precompiled with the Intel® Fortran compiler. Additionally, the Fortran 95 interfaces andwrappers are delivered as sources. (For more information, seeCompiler-dependent Functions and Fortran 90
Modules). If you are using a different compiler, build the appropriate library and modules with your compiler
and link the library as a user's library:65

5Intel® oneAPI Math Kernel Libraryfor Windows* Developer Guide

1.

Go to the respective directory <mkl directory>\interfaces\blas95 or <mkl directory>

2.

Type:

\interfaces\lapack95••

For the IA 32 architecture, make libia32 install_dir =<user_dir>
nmake libintel64 [interface=lp64|ilp64] install_dir=<user dir>
Important
The parameter install_dir is required.

As a result, the required library is built and installed in the <user dir>\lib directory, and the .mod files arebuilt and installed in the <user dir>\include\<arch>[\{lp64|ilp64}] directory, where <arch>is {ia32,
intel64} .
By default, the ifort compiler is assumed. You may change the compiler with an additional parameter of

nmake:
FC=<compiler>.
For example, the command

nmake libintel64 FC=f95 install_dir=<userf95 dir> interface=lp64
builds the required library and .mod files and installs them in subdirectories of <userf95 dir>.
To delete the library from the building directory, type:•••

For the IA-32 architecture, make cleania32 INSTALL_DIR=<user_dir>

nmake cleanintel64 [interface=lp64|ilp64] install_dir=<user dir>make clean INSTALL_DIR=<user_dir>
CautionEven if you have administrative rights, avoid setting install_dir=..\.. or install_dir=<mkl
directory> in a build or clean command above because these settings replace or delete the Intel®
oneAPI Math Kernel Library prebuilt Fortran 95 library and modules.

Compiler-dependent Functions and Fortran 90 Modules
Compiler-dependent functions occur whenever the compiler inserts into the object code function calls thatare resolved in its run-time library (RTL). Linking of such code without the appropriate RTL will result in
undefined symbols. Intel® oneAPI Math Kernel Library has been designed to minimize RTL dependencies.
In cases where RTL dependencies might arise, the functions are delivered as source code and you need tocompile the code with whatever compiler you are using for your application.
In particular, Fortran 90 modules result in the compiler-specific code generation requiring RTL support.
Therefore, Intel® oneAPI Math Kernel Library delivers these modules compiled with the Intel compiler, alongwith source code, to be used with different compilers.

Mixed-language Programming with the Intel Math Kernel
Library
Appendix A Intel® oneAPI Math Kernel Library Language Interfaces Supportlists the programming languages
supported for each Intel® oneAPI Math Kernel Library function domain. However, you can call Intel® oneAPI
Math Kernel Library routines from different language environments.
See also these Knowledge Base articles:66

Language-specific Usage Options

•
•
•

5

https://software.intel.com/content/www/us/en/develop/articles/performance-tools-for-softwaredevelopers-how-do-i-use-intel-mkl-with-java.html for how to call Intel® oneAPI Math Kernel Library from
Java* applications.
https://software.intel.com/content/www/us/en/develop/articles/how-to-use-boost-ublas-with-intelmkl.html for how to perform BLAS matrix-matrix multiplication in C++ using Intel® oneAPI Math Kernel
Library substitution of Boost* uBLAS functions.
https://software.intel.com/content/www/us/en/develop/articles/intel-mkl-and-third-party-applicationshow-to-use-them-together.html for a list of articles describing how to use Intel® oneAPI Math Kernel
Library with third-party libraries and applications.

Calling LAPACK, BLAS, and CBLAS Routines from C/C++ Language Environments
Not all Intel® oneAPI Math Kernel Library function domains support both C and Fortran environments. To useIntel® oneAPI Math Kernel Library Fortran-style functions in C/C++ environments, you should observe certain
conventions, which are discussed for LAPACK and BLAS in the subsections below.
CautionAvoid calling BLAS 95/LAPACK 95 from C/C++. Such calls require skills in manipulating the descriptorof a deferred-shape array, which is the Fortran 90 type. Moreover, BLAS95/LAPACK95 routines containlinks to a Fortran RTL.

LAPACK and BLAS
Because LAPACK and BLAS routines are Fortran-style, when calling them from C-language programs, follow
the Fortran-style calling conventions:••

Pass variables by address, not by value.
Function calls in Example "Calling a Complex BLAS Level 1 Function from C++" and Example "Using
CBLAS Interface Instead of Calling BLAS Directly from C" illustrate this.
Store your data in Fortran style, that is, column-major rather than row-major order.

With row-major order, adopted in C, the last array index changes most quickly and the first one changes
most slowly when traversing the memory segment where the array is stored. With Fortran-style columnmajor order, the last index changes most slowly whereas the first index changes most quickly (as illustrated
by the figure below for a two-dimensional array).

For example, if a two-dimensional matrix A of size mxn is stored densely in a one-dimensional array B, you
can access a matrix element like this:A[i][j] = B[i*n+j] in C
A(i,j)( i=0, ... , m-1, j=0, ... , -1)= B((j-1)*m+i) in Fortran ( i=1, ... , m, j=1, ... , n).

When calling LAPACK or BLAS routines from C, be aware that because the Fortran language is caseinsensitive, the routine names can be both upper-case or lower-case, with or without the trailing underscore.
For example, the following names are equivalent:

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LAPACK: dgetrf, DGETRF, dgetrf_, and DGETRF_
BLAS: dgemm, DGEMM, dgemm_, and DGEMM_

See Example "Calling a Complex BLAS Level 1 Function from C++" on how to call BLAS routines from C.
See also the Intel® oneAPI Math Kernel Library Developer Reference for a description of the C interface to
LAPACK functions.

CBLAS
Instead of calling BLAS routines from a C-language program, you can use the CBLAS interface.
CBLAS is a C-style interface to the BLAS routines. You can call CBLAS routines using regular C-style calls. Use
the mkl.h header file with the CBLAS interface. mkl.h includes the mkl_cblas.h header file, which specifies
enumerated values and prototypes of all the functions. It also determines whether the program is being
compiled with a C++ compiler, and if it is, the included file will be correct for use with C++ compilation.
Example "Using CBLAS Interface Instead of Calling BLAS Directly from C" illustrates the use of the CBLAS
interface.

C Interface to LAPACK
Instead of calling LAPACK routines from a C-language program, you can use the C interface to LAPACK
provided by Intel® oneAPI Math Kernel Library.
The C interface to LAPACK is a C-style interface to the LAPACK routines. This interface supports matrices inrow-major and column-major order, which you can define in the first function argument matrix_order. Use
the mkl.h header file with the C interface to LAPACK. mkl.h includes the mkl_lapacke.h header file, which
specifies constants and prototypes of all the functions. It also determines whether the program is being
compiled with a C++ compiler, and if it is, the included file will be correct for use with C++ compilation. You
can find examples of the C interface to LAPACK in the examples\lapackesubdirectory in the Intel® oneAPI
Math Kernel Library installation directory.

Using Complex Types in C/C++As described in the documentation for the Intel®Visual Fortran Compiler, C/C++ does not directly implement
the Fortran types COMPLEX(4) and COMPLEX(8). However, you can write equivalent structures. The type
COMPLEX(4) consists of two 4-byte floating-point numbers. The first of them is the real-number component,
and the second one is the imaginary-number component. The type COMPLEX(8) is similar to COMPLEX(4)except that it contains two 8-byte floating-point numbers.
Intel® oneAPI Math Kernel Library provides complex typesMKL_Complex8 and MKL_Complex16, which are
structures equivalent to the Fortran complex types COMPLEX(4) and COMPLEX(8), respectively. The
MKL_Complex8 and MKL_Complex16 types are defined in the mkl_types.h header file. You can use thesetypes to define complex data. You can also redefine the types with your own types before including the
mkl_types.h header file. The only requirement is that the types must be compatible with the Fortrancomplex layout, that is, the complex type must be a pair of real numbers for the values of real and imaginaryparts.
For example, you can use the following definitions in your C++ code:#define MKL_Complex8 std::complex<float>
and#define MKL_Complex16 std::complex<double>68

Language-specific Usage Options

5

See Example "Calling a Complex BLAS Level 1 Function from C++" for details. You can also define thesetypes in the command line:-DMKL_Complex8="std::complex<float>"-DMKL_Complex16="std::complex<double>"See Also
Intel® Software Documentation Library for the Intel®Visual Fortran Compiler documentation
for the Intel®Visual Fortran Compiler documentation

Calling BLAS Functions that Return the Complex Values in C/C++ CodeComplex values that functions return are handled differently in C and Fortran. Because BLAS is Fortran-style,you need to be careful when handling a call from C to a BLAS function that returns complex values. However,in addition to normal function calls, Fortran enables calling functions as though they were subroutines, which
provides a mechanism for returning the complex value correctly when the function is called from a C
program. When a Fortran function is called as a subroutine, the return value is the first parameter in thecalling sequence. You can use this feature to call a BLAS function from C.
The following example shows how a call to a Fortran function as a subroutine converts to a call from C andthe hidden parameter result gets exposed:
Normal Fortran function call:result = cdotc( n, x, 1, y, 1 )A call to the function as a subroutine: call cdotc( result, n, x, 1, y, 1)
A call to the function from C:

cdotc( &result, &n, x, &one, y, &one )NOTEIntel® oneAPI Math Kernel Library has both upper-case and lower-case entry points in the Fortran-style(case-insensitive) BLAS, with or without the trailing underscore. So, all these names are equivalent
and acceptable:cdotc, CDOTC, cdotc_, and CDOTC_.
The above example shows one of the ways to call several level 1 BLAS functions that return complex values
from your C and C++ applications. An easier way is to use the CBLAS interface. For instance, you can call thesame function using the CBLAS interface as follows:cblas_cdotc( n, x, 1, y, 1, &result )NOTE
The complex value comes last on the argument list in this case.
The following examples show use of the Fortran-style BLAS interface from C and C++, as well as the CBLAS(C language) interface:•••

Example "Calling a Complex BLAS Level 1 Function from C"
Example "Calling a Complex BLAS Level 1 Function from C++"
Example "Using CBLAS Interface Instead of Calling BLAS Directly from C"

Example "Calling a Complex BLAS Level 1 Function from C"
The example below illustrates a call from a C program to the complex BLAS Level 1 function zdotc(). Thisfunction computes the dot product of two double-precision complex vectors.

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In this example, the complex dot product is returned in the structure c.

#include "mkl.h"#define N 5
int main(){
int n = N, inca = 1, incb = 1, i;
MKL_Complex16 a[N], b[N], c;for( i = 0; i < n; i++ ){a[i].real = (double)i; a[i].imag = (double)i * 2.0;b[i].real = (double)(n - i); b[i].imag = (double)i * 2.0;}
zdotc( &c, &n, a, &inca, b, &incb );printf( "The complex dot product is: ( %6.2f, %6.2f)\n", c.real, c.imag );return 0;}
In this example, the complex dot product for large data size is returned in the structure c.

#include "mkl.h"#define N 5
int main(){
MKL_INT64 n = N, inca = 1, incb = 1, i;
MKL_Complex16 a[N], b[N], c;for( i = 0; i < n; i++ ){a[i].real = (double)i; a[i].imag = (double)i * 2.0;b[i].real = (double)(n - i); b[i].imag = (double)i * 2.0;}
zdotc_64( &c, &n, a, &inca, b, &incb );printf( "The complex dot product is: ( %6.2f, %6.2f)\n", c.real, c.imag );return 0;}

Example "Calling a Complex BLAS Level 1 Function from C++"
Below is the C++ implementation:#include <complex>#include <iostream>#define MKL_Complex16 std::complex<double>#include "mkl.h"#define N 5
int main(){
int n, inca = 1, incb = 1, i;
std::complex<double> a[N], b[N], c;n = N;
for( i = 0; i < n; i++ ){a[i] = std::complex<double>(i,i*2.0);b[i] = std::complex<double>(n-i,i*2.0);}70Language-specific Usage Options

5

zdotc(&c, &n, a, &inca, b, &incb );std::cout << "The complex dot product is: " << c << std::endl;
return 0;}

Example "Using CBLAS Interface Instead of Calling BLAS Directly from C"
This example uses CBLAS:#include <stdio.h>#include "mkl.h"
typedef struct{ double re; double im; } complex16;#define N 5
int main(){
int n, inca = 1, incb = 1, i;
complex16 a[N], b[N], c;n = N;
for( i = 0; i < n; i++ ){a[i].re = (double)i; a[i].im = (double)i * 2.0;b[i].re = (double)(n - i); b[i].im = (double)i * 2.0;}
cblas_zdotc_sub(n, a, inca, b, incb, &c );printf( "The complex dot product is: ( %6.2f, %6.2f)\n", c.re, c.im );return 0;
}

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Obtaining Numerically
Reproducible Results

6Intel® oneAPI Math Kernel Library (oneMKL) offers functions and environment variables that help you obtain
Conditional Numerical Reproducibility (CNR) of floating-point results when calling the library functions fromyour application. These new controls enable Intel® oneAPI Math Kernel Library to run in a special mode, whenfunctions return bitwise reproducible floating-point results from run to run under the following conditions:••

Calls to Intel® oneAPI Math Kernel Library occur in a single executable
The number of computational threads used by the library does not change in the run

For a limited set of routines, you can eliminate the second condition by using Intel® oneAPI Math Kernel
Library in strict CNR mode.
It is well known that for general single and double precision IEEE floating-point numbers, the associative
property does not always hold, meaning (a+b)+c may not equal a +(b+c). Let's consider a specific example.
In infinite precision arithmetic 2-63 + 1 + -1 = 2-63. If this same computation is done on a computer using
double precision floating-point numbers, a rounding error is introduced, and the order of operations becomes
important:(2-63 + 1) + (-1) ≃ 1 + (-1) = 0
versus2-63 + (1 + (-1)) ≃ 2-63 + 0 = 2-63
This inconsistency in results due to order of operations is precisely what the new functionality addresses.
The application related factors that affect the order of floating-point operations within a single executable
program include selection of a code path based on run-time processor dispatching, alignment of data arrays,
variation in number of threads, threaded algorithms and internal floating-point control settings. You cancontrol most of these factors by controlling the number of threads and floating-point settings and by taking
steps to align memory when it is allocated (see the Getting Reproducible Results with Intel® MKL knowledge
base article for details). However, run-time dispatching and certain threaded algorithms do not allow users tomake changes that can ensure the same order of operations from run to run.
Intel® oneAPI Math Kernel Library does run-time processor dispatching in order to identify the appropriate
internal code paths to traverse for the Intel® oneAPI Math Kernel Library functions called by the application.
The code paths chosen may differ across a wide range of Intel processors and Intel architecture compatible
processors and may provide differing levels of performance. For example, an Intel® oneAPI Math Kernel
Library function running on an Intel® Pentium® 4 processor may run one code path, while on the latest Intel®
Xeon® processor it will run another code path. This happens because each unique code path has been
optimized to match the features available on the underlying processor. One key way that the new features ofa processor are exposed to the programmer is through the instruction set architecture (ISA). Because of this,code branches in Intel® oneAPI Math Kernel Library are designated by the latest ISA they use for
optimizations: from the Intel® Streaming SIMD Extensions 2 (Intel® SSE2) to the Intel® Advanced Vector
Extensions2 (Intel® AVX2). The feature-based approach introduces a challenge: if any of the internal floatingpoint operations are done in a different order or are re-associated, the computed results may differ.
Dispatching optimized code paths based on the capabilities of the processor on which the code is running iscentral to the optimization approach used by Intel® oneAPI Math Kernel Library. So it is natural thatconsistent results require some performance trade-offs. If limited to a particular code path, performance ofIntel® oneAPI Math Kernel Library can in some circumstances degrade by more than a half. To understandthis, note that matrix-multiply performance nearly doubled with the introduction of new processors
supporting Intel AVX2 instructions. Even if the code branch is not restricted, performance can degrade by
10-20% because the new functionality restricts algorithms to maintain the order of operations.
Product and Performance Information
Performance varies by use, configuration and other factors. Learn more at www.Intel.com/
PerformanceIndex.

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Product and Performance Information
Notice revision #20201201

Getting Started with Conditional Numerical Reproducibility
Intel® oneAPI Math Kernel Library offers functions and environment variables to help you get reproducible
results. You can configure Intel® oneAPI Math Kernel Library using functions or environment variables, but
the functions provide more flexibility.
The following specific examples introduce you to the conditional numerical reproducibility.
While these examples recommend aligning input and output data, you can supply unaligned data to Intel®
oneAPI Math Kernel Library functions running in the CNR mode, but refer toReproducibility Conditions fordetails related to data alignment.

Intel CPUs supporting Intel AVX2
To ensure Intel® oneAPI Math Kernel Library calls return the same results on every Intel CPU supporting Intel
AVX2 instructions:
1.
2.
3.

Make sure that your application uses a fixed number of threads(Recommended) Properly align input and output arrays in Intel® oneAPI Math Kernel Library function
calls
Do either of the following:•Callmkl_cbwr_set(MKL_CBWR_AVX2)•

Set the environment variable:

set MKL_CBWR = AVX2
NOTE
On non-Intel CPUs and on Intel CPUs that do not support Intel AVX2, this environment setting may
cause results to differ because the AUTO branch is used instead, while the above function call returns
an error and does not enable the CNR mode.

Intel CPUs supporting Intel SSE2
To ensure Intel® oneAPI Math Kernel Library calls return the same results on every Intel CPU supporting Intel
SSE2instructions:
1.
2.
3.

Make sure that your application uses a fixed number of threads(Recommended) Properly align input and output arrays in Intel® oneAPI Math Kernel Library function
calls
Do either of the following:•Callmkl_cbwr_set(MKL_CBWR_SSE2)•

Set the environment variable:

set MKL_CBWR = SSE2

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NOTE
On non-Intel CPUs, this environment setting may cause results to differ because the AUTO branch isused instead, while the above function call returns an error and does not enable the CNR mode.

Intel or Intel compatible CPUs supporting Intel SSE2
On non-Intel CPUs, only the MKL_CBWR_AUTO and MKL_CBWR_COMPATIBLE options are supported for function
calls and only AUTO and COMPATIBLE options for environment settings.
To ensure Intel® oneAPI Math Kernel Library calls return the same results on all Intel or Intel compatible
CPUs supporting Intel SSE2 instructions:
1.
2.
3.

Make sure that your application uses a fixed number of threads(Recommended) Properly align input and output arrays in Intel® oneAPI Math Kernel Library function
calls
Do either of the following:•Callmkl_cbwr_set(MKL_CBWR_COMPATIBLE)•

Set the environment variable:

set MKL_CBWR = COMPATIBLE
NOTEThe special MKL_CBWR_COMPATIBLE/COMPATIBLEoption is provided because Intel and Intel compatible
CPUs have a few instructions, such as approximation instructions rcpps/rsqrtps, that may return
different results. This option ensures that Intel® oneAPI Math Kernel Library does not use these
instructions and forces a single Intel SSE2 only code path to be executed.

Next steps
See Specifying the Code Branches

for details of specifying the branch using
environment variables.

See the following sections in the Intel® oneAPI Math Kernel Library Developer Reference:
Support Functions for Conditional Numerical Reproducibility

for how to configure the CNR mode ofIntel® oneAPI Math Kernel Library using
functions.

Intel® oneAPI Math Kernel Library PARDISO - Parallel Direct
Sparse Solver Interface

for how to configure the CNR mode for
PARDISO.

See Also
Code Examples

Specifying Code BranchesIntel® oneAPI Math Kernel Library provides a conditional numerical reproducibility (CNR) functionality thatenables you to obtain reproducible results from oneMKL routines. When enabling CNR, you choose a specificcode branch of Intel® oneAPI Math Kernel Library that corresponds to the instruction set architecture (ISA)
that you target. You can specify the code branch and other CNR options using theMKL_CBWR environment
variable.

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Obtaining Numerically Reproducible Results••

6

MKL_CBWR="<branch>[,STRICT]" or
MKL_CBWR="BRANCH=<branch>[,STRICT]"Use the STRICT flag to enable strict CNR mode. For more information, see Reproducibility Conditions.
The <branch> placeholder specifies the CNR branch with one of the following values:
Value

Description

AUTO

CNR mode uses the standard ISA-based dispatching model while
ensuring fixed cache sizes, deterministic reductions, and static
scheduling

COMPATIBLEIntel® Streaming SIMD Extensions 2 (Intel® SSE2) without rcpps/
rsqrtps instructions

SSE2Intel SSE2

SSE3

DEPRECATED. Intel® Streaming SIMD Extensions 3 (Intel® SSE3). Thissetting is kept for backward compatibility and is equivalent to SSE2.

SSSE3

Supplemental Streaming SIMD Extensions 3 (SSSE3)

SSE4_2

Intel® Streaming SIMD Extensions 4.2 (Intel® SSE4.2)

AVXIntel® Advanced Vector Extensions (Intel® AVX)

AVX2Intel® Advanced Vector Extensions 2 (Intel® AVX2)

AVX512

Intel AVX-512 on Intel® Xeon® processors

AVX512_E1

Intel® Advanced Vector Extensions 512 (Intel® AVX-512) with support
for Vector Neural Network Instructions

AVX512_MIC

DEPRECATED. Intel® Advanced Vector Extensions 512 (Intel®
AVX-512) on Intel® Xeon Phi™ processors. This setting is kept for
backward compatibility and is equivalent to AVX2.

AVX512_MIC_E1

DEPRECATED. Intel® Advanced Vector Extensions 512 (Intel®
AVX-512) with support for Vector Neural Network Instructions onIntel® Xeon Phi™ processors. This setting is kept for backward
compatibility and is equivalent to AVX2.

When specifying the CNR branch, be aware of the following:••••

Reproducible results are provided under Reproducibility Conditions.
Settings other than AUTO or COMPATIBLE are available only for Intel processors.
To get the CNR branch optimized for the processor where your program is currently running, choose thevalue of AUTO or call the mkl_cbwr_get_auto_branch function.
Strict CNR mode is supported only for AVX2, AVX512, AVX512_E1, AVX512_MIC, and AVX512_MIC_E1
branches. You can also use strict CNR mode with the AUTO branch when running on Intel processors that
support one of these instruction set architectures (ISAs).

Setting the MKL_CBWR environment variable or a call to an equivalent mkl_cbwr_set function fixes the code
branch and sets the reproducibility mode.

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NOTE
•

•
•

If the value of the branch is incorrect or your processor or operating system does not support the
specified ISA, CNR ignores this value and uses the AUTO branch without providing any warning
messages.
Calls to functions that define the behavior of CNR must precede any of the math library functions
that they control.
Settings specified by the functions take precedence over the settings specified by the environment
variable.

See the Intel® oneAPI Math Kernel Library Developer Reference for how to specify the branches using
functions.

See Also
Getting Started with Conditional Numerical Reproducibility

Reproducibility Conditions
To get reproducible results from run to run, ensure that the number of threads is fixed and constant.
Specifically:•••

If you are running your program with OpenMP* parallelization on different processors, explicitly specify
the number of threads.
To ensure that your application has deterministic behavior with OpenMP* parallelization and does not
adjust the number of threads dynamically at run time, set MKL_DYNAMIC and OMP_DYNAMIC to FALSE. Thisis especially needed if you are running your program on different systems.
If you are running your program with the Intel® Threading Building Blocks parallelization, numericalreproducibility is not guaranteed.

Strict CNR ModeIn strict CNR mode, oneAPI Math Kernel Library provides bitwise reproducible results for a limited set offunctions and code branches even when the number of threads changes. These routines and branches
support strict CNR mode (64-bit libraries only):••?gemm, ?symm, ?hemm, ?trsm and their CBLAS equivalents (cblas_?gemm, cblas_?symm, cblas_?hemm,
and cblas_?trsm).
Intel® Advanced Vector Extensions 2 (Intel® AVX2) or Intel® Advanced Vector Extensions 512 (Intel®
AVX-512).

When using other routines or CNR branches,oneAPI Math Kernel Library operates in standard (non-strict)
CNR mode, subject to the restrictions described above. Enabling strict CNR mode can reduce performance.
NOTE
•

•
•

As usual, you should align your data, even in CNR mode, to obtain the best possible performance.
While CNR mode also fully supports unaligned input and output data, the use of it might reduce theperformance of some oneAPI Math Kernel Library functions on earlier Intel processors. Refer tocoding techniques that improve performance for more details.
Conditional Numerical Reproducibility does not ensure that bitwise-identical NaN values are
generated when the input data contains NaN values.
If dynamic memory allocation fails on one run but succeeds on another run, you may fail to get
reproducible results between these two runs.

See Also
MKL_DYNAMIC
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6

Coding Techniques

Setting the Environment Variable for Conditional
Numerical Reproducibility
The following examples illustrate the use of the MKL_CBWR environment variable. The first command sets
Intel® oneAPI Math Kernel Library to run in the CNR mode based on the default dispatching for your platform.
The other two commandsare equivalent and set the CNR branch to Intel AVX:•••

set MKL_CBWR=AUTO
set MKL_CBWR=AVX
set MKL_CBWR=BRANCH=AVX

See Also
Specifying Code Branches

Code Examples
The following simple programs show how to obtain reproducible results from run to run of Intel® oneAPI Math
Kernel Library functions. See theIntel® oneAPI Math Kernel Library Developer Reference for more examples.

C Example of CNR#include <mkl.h>
int main(void) {int my_cbwr_branch;/* Align all input/output data on 64-byte boundaries *//* "for best performance of Intel® oneAPI Math Kernel Library */void *darray;int darray_size=1000;/* Set alignment value in bytes */int alignment=64;/* Allocate aligned array */darray = mkl_malloc (sizeof(double)*darray_size, alignment);/* Find the available MKL_CBWR_BRANCH automatically */my_cbwr_branch = mkl_cbwr_get_auto_branch();/* User code without oneMKL calls *//* Piece of the code where CNR of oneMKL is needed *//* The performance of oneMKL functions might be reduced for CNR mode *//* If the "IF" statement below is commented out, Intel® oneAPI Math Kernel Library will run in a
regular mode, *//* and data alignment will allow you to get best performance */if (mkl_cbwr_set(my_cbwr_branch)) {printf("Error in setting MKL_CBWR_BRANCH! Aborting…\n”);
return;}/* CNR calls to oneMKL + any other code *//* Free the allocated aligned array */mkl_free(darray);}Fortran Example of CNR
PROGRAM MAIN
INCLUDE 'mkl.fi'
INTEGER*4 MY_CBWR_BRANCH! Align all input/output data on 64-byteboundaries
776Intel® oneAPI Math Kernel Libraryfor Windows* Developer Guide! "for best performance of Intel® oneAPI Math Kernel Library! Declare oneMKL memory allocation routine#ifdef _IA32
INTEGER MKL_MALLOC#else
INTEGER*8 MKL_MALLOC#endif
EXTERNAL MKL_MALLOC, MKL_FREEDOUBLE PRECISION DARRAY
POINTER (P_DARRAY,DARRAY(1))
INTEGER DARRAY_SIZEPARAMETER (DARRAY_SIZE=1000)! Set alignment value in bytes
INTEGER ALIGNMENTPARAMETER (ALIGNMENT=64)! Allocate aligned array
P_DARRAY = MKL_MALLOC (%VAL(8*DARRAY_SIZE), %VAL(ALIGNMENT));! Find the available MKL_CBWR_BRANCH automatically
MY_CBWR_BRANCH = MKL_CBWR_GET_AUTO_BRANCH()! User code without oneMKL calls! Piece of the code where CNR of oneMKL is needed! The performance of oneMKL functions may be reduced for CNR mode
! If the "IF" statement below is commented out, Intel® oneAPI Math Kernel Library will run in a
regular mode,! and data alignment will allow you to get best performance
IF (MKL_CBWR_SET (MY_CBWR_BRANCH) .NE. MKL_CBWR_SUCCESS) THEN
PRINT *, 'Error in setting MKL_CBWR_BRANCH! Aborting…'
RETURNENDIF! CNR calls to oneMKL + any other code
! Free the allocated aligned array
CALL MKL_FREE(P_DARRAY)
END

Use of CNR with Unaligned Data in C
#include <mkl.h>
int main(void) {int my_cbwr_branch;/* If it is not possible to align all input/output data on 64-byte boundaries *//* to achieve performance, use unaligned IO data with possible performance *//* penalty *//* Using unaligned IO data */double *darray;int darray_size=1000;/* Allocate array, malloc aligns data on 8/16-byte boundary only */darray = (double *)malloc (sizeof(double)*darray_size);/* Find the available MKL_CBWR_BRANCH automatically */my_cbwr_branch = mkl_cbwr_get_auto_branch();/* User code without oneMKL calls *//* Piece of the code where CNR of oneMKL is needed *//* The performance of oneMKL functions might be reduced for CNR mode *//* If the "IF" statement below is commented out, oneMKL will run in a regular mode, *//* and you will NOT get best performance without data alignment */if (mkl_cbwr_set(my_cbwr_branch)) {printf("Error in setting MKL_CBWR_BRANCH! Aborting…\n");return;78

Obtaining Numerically Reproducible Results

6}/* CNR calls to oneMKL + any other code *//* Free the allocated array */free(darray);Use of CNR with Unaligned Data in Fortran
PROGRAM MAIN
INCLUDE 'mkl.fi'
INTEGER*4 MY_CBWR_BRANCH! If it is not possible to align all input/output data on 64-byte boundaries! to achieve performance, use unaligned IO data with possible performance! penalty
DOUBLE PRECISION, DIMENSION(:), ALLOCATABLE :: DARRAYINTEGER DARRAY_SIZE, STATUS
PARAMETER (DARRAY_SIZE=1000)! Allocate array with undefined alignment
ALLOCATE(DARRAY(DARRAY_SIZE));! Find the available MKL_CBWR_BRANCH automatically
MY_CBWR_BRANCH = MKL_CBWR_GET_AUTO_BRANCH()! User code without oneMKL calls! Piece of the code where CNR of oneMKL is needed! The performance of oneMKL functions might be reduced for CNR mode
! If the "IF" statement below is commented out, oneMKL will run in a regular mode,! and you will NOT get best performance without data alignment
IF (MKL_CBWR_SET(MY_CBWR_BRANCH) .NE. MKL_CBWR_SUCCESS) THEN
PRINT *, 'Error in setting MKL_CBWR_BRANCH! Aborting…'
RETURNENDIF! CNR calls to oneMKL + any other code
! Free the allocated array
DEALLOCATE(DARRAY)
END

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Coding Tips

7

This section provides coding tips for managing data alignment and version-specific compilation.

See Also
Mixed-language Programming with the Intel® oneAPI Math Kernel Library Tips on languagespecific programming
Managing Performance and Memory Coding tips related to performance improvement and use ofmemory functions
Obtaining Numerically Reproducible Results Tips for obtaining numerically reproducible results ofcomputations

Example of Data Alignment
Needs for best performance with Intel® oneAPI Math Kernel Library or for reproducible results from run to runof Intel® oneAPI Math Kernel Library functions require alignment of data arrays. The following example shows
how to align an array on 64-byte boundaries. To do this, usemkl_malloc() in place of system provided
memory allocators, as shown in the code example below.

Aligning Addresses on 64-byte Boundaries// ******* C language *******...
#include <stdlib.h>#include <mkl.h>...
void *darray;
int workspace;// Set value of alignment
int alignment=64;...
// Allocate aligned workspacedarray = mkl_malloc( sizeof(double)*workspace, alignment );...
// call the program using oneMKL
mkl_app( darray );...
// Free workspace
mkl_free( darray );! ******* Fortran language *******...
! Set value of alignment
integeralignment
parameter (alignment=64)...
! Declare oneMKL routines#ifdef _IA32
integer mkl_malloc
#else
80

Coding Tips

7

integer*8 mkl_malloc#endifexternal mkl_malloc, mkl_free, mkl_app...
double precision darraypointer (p_wrk,darray(1))
integer workspace...
! Allocate aligned workspace
p_wrk = mkl_malloc( %val(8*workspace), %val(alignment) )...
! call the program using oneMKL
call mkl_app( darray )...
! Free workspacecall mkl_free(p_wrk)

Using Predefined Preprocessor Symbols for Intel® MKL
Version-Dependent Compilation
Preprocessor symbols (macros) substitute values in a program before it is compiled. The substitution is
performed in the preprocessing phase.
The following preprocessor symbols are available:
Predefined Preprocessor Symbol

Description__INTEL_MKL__Intel® oneAPI Math Kernel Library major version__INTEL_MKL_MINOR__Intel® oneAPI Math Kernel Library minor version__INTEL_MKL_UPDATE__

Intel® oneAPI Math Kernel Library update number

INTEL_MKL_VERSIONIntel® oneAPI Math Kernel Library full version in thefollowing format:

INTEL_MKL_VERSION =(__INTEL_MKL__*100+__INTEL_MKL_MINOR__)*100+__I
NTEL_MKL_UPDATE__
These symbols enable conditional compilation of code that uses new features introduced in a particularversion of the library.
To perform conditional compilation:
1.

Depending on your compiler, include in your code the file where the macros are defined:
C/C++ compiler:

mkl_version.h,or mkl.h, which includes mkl_version.hIntel®Fortran compiler:

mkl.fiAny Fortran compiler with enabled
preprocessing:mkl_version.h
Read the documentation for your compiler for the option thatenables preprocessing.

2.

[Optionally] Use the following preprocessor directives to check whether the macro is defined:3.

• #ifdef, #endif for C/C++• !DEC$IF DEFINED, !DEC$ENDIF for FortranUse preprocessor directives for conditional inclusion of code:

81

7
•
•

Intel® oneAPI Math Kernel Libraryfor Windows* Developer Guide#if, #endif for C/C++!DEC$IF, !DEC$ENDIF for Fortran

ExampleThis example shows how to compile a code segment conditionally for a specific version of Intel® oneAPI Math
Kernel Library. In this case, the version is 11.2 Update 4:Intel®Fortran Compiler:

include "mkl.fi"!DEC$IF DEFINED INTEL_MKL_VERSION!DEC$IF INTEL_MKL_VERSION .EQ. 110204*
Code to be conditionally compiled!DEC$ENDIF!DEC$ENDIF
C/C++ Compiler. Fortran Compiler with Enabled Preprocessing:#include "mkl.h"#ifdef INTEL_MKL_VERSION#if INTEL_MKL_VERSION == 110204...
Code to be conditionally compiled#endif#endif82

Managing Output

8

8Managing Output
Using oneMKL Verbose Mode

When building applications that call Intel® oneAPI Math Kernel Library functions, it may be useful todetermine:••••

which computational functions are called
what parameters are passed to them
how much time is spent to execute the functions(for GPU applications) which GPU device the kernel is executed on

You can get an application to print this information to a standard output device by enabling Intel® oneAPI
Math Kernel Library Verbose. Functions that can print this information are referred to as verbose-enabledfunctions.
When Verbose mode is active in an Intel® oneAPI Math Kernel Library domain, every call of a verboseenabled function finishes with printing a human-readable line describing the call. However, if your application
gets terminated for some reason during the function call, no information for that function will be printed. The
first call to a verbose-enabled function also prints a version information line.
For GPU applications, additional information (one or more GPU information lines) will also be printed by the
first call to a verbose-enabled function, following the version information line which will be printed for thehost CPU. If there is more than one GPU detected, each GPU device will be printed in a separate line.
We have different implementations for verbose with CPU applications and verbose with GPU applications. The
Intel® MKL Verbose mode has 2 modes when used with CPU applications: disabled (default) and enabled. The
Intel® MKL Verbose mode has three modes when used with GPU applications: disabled (default), enabled
without timing, and enabled with synchronous timing.
To change the verbose mode, either set the environment variable MKL_VERBOSE:
CPU application

GPU application

Set MKL_VERBOSE to 0

to disable Verbose

to disable Verbose

Set MKL_VERBOSE to 1

to enable Verbose

to enable Verbose without timing

Set MKL_VERBOSE to 2

to enable Verbose

to enable Verbose with synchronous
timingor call the support function mkl_verbose(int mode):
CPU application

GPU application

Call mkl_verbose(0)to disable Verbose

to disable Verbose

Call mkl_verbose(1)to enable Verbose

to enable Verbose without timing

Call mkl_verbose(2)to enable Verbose

to enable Verbose with synchronous
timingVerbose with CPU Applications
Verbose output will be consisted of version information line and call description lines for CPU.
For CPU applications, you can enable Intel® oneAPI Math Kernel Library Verbose mode in these domains:••••

BLAS (and BLAS-like extensions)
LAPACK
ScaLAPACK (selected functionality)
FFT

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Intel® oneAPI Math Kernel Libraryfor Windows* Developer Guide

Verbose with GPU Applications
The verbose feature is enabled for GPU applications that uses DPC++ API or C/Fortran API with OpenMP
offload. When used with GPU applications, verbose allows the measurement of execution time to be enabledor disabled with verbose mode. Timing is taken synchronously, so if verbose is enabled with timing, kernel
executions will become synchronous (previous kernel will block later kernels)
Verbose output will be consisted of version information line, GPU information lines, and call description linesfor GPU.
NOTE Timing for GPU applications is reported for overall execution. For selected functionality device
execution time can be also reported if the input queue was created with profiling information.

For GPU applications, you can enable Intel® oneAPI Math Kernel Library Verbose mode in these domains:•••

BLAS (and BLAS-like extensions)
LAPACKFFTFor Both CPU and GPU Verbose
Both enabling and disabling of the Verbose mode using the function call takes precedence over the®
environment setting. For a full description of the mkl_verbose function, see either the Intel oneAPI Math®
Kernel Library Developer Reference for C or the Intel oneAPI Math Kernel Library Developer Reference for®
Fortran. Both references are available in the Intel Software Documentation Library.
Intel® oneAPI Math Kernel Library Verbose mode is not a thread-local but a global state. In other words, if anapplication changes the mode from multiple threads, the result is undefined.
WARNING
The performance of an application may degrade with the Verbose mode enabled, especially when thenumber of calls to verbose-enabled functions is large, because every call to a verbose-enabledfunction requires an output operation.

See Also
Intel Software Documentation Library

Version Information LineIn the Intel® oneAPI Math Kernel Library Verbose mode, the first call to a verbose-enabled function prints a
version information line. The line begins with theMKL_VERBOSE character string and uses spaces asdelimiters. The format of the rest of the line may change in a future release.
The following table lists information contained in a version information line and provides available links formore information:
Information

Description

Intel® oneAPI Math Kernel
Library version.

This information is separated by a comma from the restof the line.

Operating system.

Possible values:
•
•
•

84

Lnx for Linux* OS
Win for Windows* OS
OSX for macOS*

Related Links

Managing Output

Information

8

Description

Related Links

Possible values:

Using the ILP64
Interface vs. LP64
Interface

The host CPU frequency.
Intel® oneAPI Math Kernel
Library interface layer
used by the application.

•
•

cdecl
lp64 or ilp64 on systems based on the Intel® 64
architecture.

Intel® oneAPI Math Kernel
Library threading layer
used by the application.

Possible values:

intel_thread, tbb_thread, or sequential.

Linking withThreading Libraries

The following is an example of a version information line:

MKL_VERBOSE Intel(R) MKL 11.2 Beta build 20131126 for Intel(R) 64 architecture Intel(R)Advanced Vector Extensions (Intel(R) AVX) Enabled Processor, Win 3.10GHz lp64
intel_thread

GPU Information LineIn Intel® oneAPI Math Kernel Library Verbose mode for GPU applications, the first call to a verbose-enabledfunction prints out the GPU information line or lines for all detected GPU devices, each in a separate line. Theline begins with the MKL_VERBOSE Detected character string and uses spaces as delimiters. The format of
the rest of the line may change in a future release.
®

Only Intel GPU is supported.
The following table lists information contained in a GPU information line.
See AlsoCall Description Line for GPU
Information

Description

GPU index

The index of the GPU device will be printed after thecharacter string "GPU" (e.g. GPU0, GPU1, GPU2, etc). ThisGPU index will be used as a nickname of the device in call
description lines to refer to the device.

®

Intel GPU architecture

The value can be one of the following:
•
•
•
•
•
•

Intel(R) Gen9Intel(R) Xe_LP
Intel(R) Xe_HP
Intel(R) Xe_HPG
Intel(R) Xe_HPC
Unknown GPU

Runtime backend

The value printed is prefixed with Backend:

Vector Engine number

The value printed is prefixed with VE:

Stack number

The value printed is prefixed with Stack:Maximum workgroup size

The value printed is prefixed with maxWGsize:

The following is an example of a GPU information line:

MKL_VERBOSE Detected GPU0 Intel(R)_Gen9 Backend:OpenCL VE:72 Stack:1 maxWGsize:25685

8Intel® oneAPI Math Kernel Libraryfor Windows* Developer Guide

Call Description Line
Call Description Line for CPU
In Intel® oneAPI Math Kernel Library Verbose mode, each verbose-enabled function called from yourapplication prints a call description line. The line begins with the MKL_VERBOSE character string and uses
spaces as delimiters. The format of the rest of the line is subject to change in a future release.
The following table lists information contained in a call description line for Verbose with CPU applications andprovides available links for more information:
Information

Description

The name of thefunction.

Although the name printed may differ from the name used in
the source code of the application (for example, the cblas_
prefix of CBLAS functions is not printed), you can easily
recognize the function by the printed name.

Values of thearguments.

•

•
•
•
•

•

Time taken by thefunction.

•••

Value of the

The values are listed in the order of the formal argument list.
The list directly follows the function name, it is parenthesized
and comma-separated.
Arrays are printed as addresses (to see the alignment of thedata).
Integer scalar parameters passed by reference are printed byvalue. Zero values are printed for NULL references.
Character values are printed without quotes.
For all parameters passed by reference, the values printed
are the values returned by the function. For example, theprinted value of the info parameter of a LAPACK function is
its value after the function execution.
For verbose-enabled functions in the ScaLAPACK domain, in
addition to the standard input parameters, information about
blocking factors, MPI rank, and process grid is also printed.
The time is printed in convenient units (seconds,
milliseconds, and so on), which are explicitly indicated.
The time may fluctuate from run to run.
The time printed may occasionally be larger than the time
actually taken by the function call, especially for small
problem sizes and multi-socket machines.To reduce thiseffect, bind threads that call Intel® oneAPI Math Kernel
Library to CPU cores by setting an affinity mask.

Getting Started
with Conditional
Numerical
Reproducibility

The value printed is prefixed with Dyn:

MKL_DYNAMIC

environment
variable.
Value of theenvironment
variable.

86

Managing Multicore Performancefor options to set
an affinity mask.

The value printed is prefixed with CNR:

MKL_CBWRMKL_DYNAMIC

Related Links

Managing Output

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Information

Description

Related Links

Status of theIntel® oneAPI
Math Kernel
Librarymemory
manager.

The value printed is prefixed with FastMM:

Avoiding Memory
Leaks in
oneMKLfor a
description of theIntel® oneAPI
Math Kernel
Librarymemory
manager

OpenMP* thread
number of thecalling thread.

The value printed is prefixed with TID:

Values of Intel®
oneAPI Math
Kernel Libraryenvironment
variables defining
the general anddomain-specificnumbers of
threads,
separated by acomma.

The first value printed is prefixed with NThr:

oneMKL-specific
Environment
Variables for
Threading Control

The following is an example of a call description line (with OpenMP threading):

MKL_VERBOSE
DGEMM(n,n,1000,1000,240,0x7ffff708bb30,0x7ff2aea4c000,1000,0x7ff28e92b000,240,0x7ffff70
8bb38,0x7ff28e08d000,1000) 1.66ms CNR:OFF Dyn:1 FastMM:1 TID:0 NThr:16
The following is an example of a call description line (with TBB threading):

MKL_VERBOSE
DGEMM(n,n,1000,1000,240,0x7ffff708bb30,0x7ff2aea4c000,1000,0x7ff28e92b000,240,0x7ffff70
8bb38,0x7ff28e08d000,1000) 1.66ms CNR:OFF Dyn:1 FastMM:1
NOTE For more information about selected threading, refer to Version Information Line.

The following information is not printed because of limitations of Intel® oneAPI Math Kernel Library Verbose
mode:•

Input values of parameters passed by reference if the values were changed by the function.

•

For example, if a LAPACK function is called with a workspace query, that is, the value of the lwork
parameter equals -1 on input, the call description line prints the result of the query and not -1.
Return values of functions.

•

For example, the value returned by the function ilaenv is not printed.
Floating-point scalars passed by reference.

878Intel® oneAPI Math Kernel Libraryfor Windows* Developer Guide

Call Description Line for GPU
In Intel® oneAPI Math Kernel Library Verbose mode, each verbose-enabled function called from yourapplication prints a call description line. The line begins with the MKL_VERBOSE character string and uses
spaces as delimiters. The format of the rest of the line may change in a future release.
The following table lists information contained in a call description line for verbose with GPU applications.
See AlsoGPU Information Line
Information

Description

The name of the function

Although the name printed may differ from the name usedin the source code of the application, you can easily
recognize the function by the printed name.

The values of the arguments

•

•
•

•
•

Time taken by the function

•

•
•

•
•

Device index

The values are listed in the order of the formal
argument list. The list directly follows thefunction name, and it is parenthesized andcomma-separated.
Arrays are printed as addresses (to show thealignment of the data).
Integer scalar parameters passed by reference
are printed by value. Zero values are printed for
NULL references.
Character values are printed without quotation
marks.
For all parameters passed by reference, thevalues printed are the values returned by thefunction.
If verbose is enabled with timing for GPU
applications, kernel executions will become
synchronous (previous kernel will block later
kernels) and the measured time may include
potential data transfers and/or data copies inhost and devices.
If Verbose is enabled without timing for GPU
applications, time will be printed out as 0.
The time is printed in convenient units (seconds,
milliseconds, and so on), which are explicitly
indicated.
The time may fluctuate from run to run.
The time printed may occasionally be larger than
the time actually taken by the function call,especially for small problem sizes.

The index of the GPU device on which the kernel isbeing executed will be printed after the character
string "GPU" (e.g. GPU0, GPU1, GPU2, etc). Use theindex and refer to the GPU information lines formore information about the specific device.
If the kernel is executed on the host CPU, this field
will be empty.

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The following is an example of a call description line:

MKL_VERBOSE FFT(dcfi64) 224.30us GPU0
Some Limitations
For GPU applications, the call description lines may be printed out-of-order (the order of the call
description lines printed in the verbose output may not be the order in which the kernels are
submitted in the functions) for the following two cases:••

Verbose is enabled without timing and the kernel executions stay asynchronous.
The kernel is not executed on one of the GPU devices, but on the host CPU (the device index will
not be printed in this case).

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Working with the Intel® oneAPI
Math Kernel Library Cluster
Software

9Intel® oneAPI Math Kernel Library (oneMKL) includes distributed memory function domains for use onclusters:•••

ScaLAPACK
Cluster Fourier Transform Functions (Cluster FFT)
Parallel Direct Sparse Solvers for Clusters (Cluster Sparse Solver)

ScaLAPACK, Cluster FFT, and Cluster Sparse Solver are only provided for the Intel® 64 and Intel® Many
Integrated Core architectures.
Product and Performance Information
Performance varies by use, configuration and other factors. Learn more at www.Intel.com/
PerformanceIndex.
Notice revision #20201201

See Also
Intel® oneAPI Math Kernel Library StructureManaging Performance of the Cluster Fourier Transform Functions
Intel® Distribution for LINPACK* Benchmark

Message-Passing Interface Support
Intel® oneAPI Math Kernel Library ScaLAPACK, Cluster FFT, and Cluster Sparse Solver support
implementations of the message-passing interface (MPI) identified in theIntel® oneAPI Math Kernel Library(oneMKL) Release Notes.
To link applications with ScaLAPACK, Cluster FFT, or Cluster Sparse Solver, you need to configure your systemdepending on your message-passing interface (MPI) implementation as explained below.
If you are using the Microsoft MPI, do the following:
1.
2.
3.

Add Microsoft Compute Cluster Pack\include to the include path (assuming the default
installation of the Microsoft MPI).
Add Microsoft Compute Cluster Pack\Lib\AMD64 to the library path.
Add msmpi.lib to your link command.

If you are using the Intel® MPI, do the following:
1.

2.
3.

Add the following string to the include path: %ProgramFiles%\Intel\MPI\<ver>\intel64\include,
where <ver> is the directory for a particular MPI version, for example, %ProgramFiles%\Intel\MPI\5.1\intel64\include.
Add the following string to the library path: %ProgramFiles%\Intel\MPI\<ver>\intel64\lib, forexample, %ProgramFiles%\Intel\MPI\5.1\intel64\lib.
Add impi.lib and impicxx.lib to your link command.

Check the documentation that comes with your MPI implementation for implementation-specific details oflinking.

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Linking with oneMKL Cluster Software
The Intel® oneAPI Math Kernel Library ScaLAPACK, Cluster FFT, and Cluster Sparse Solver support MPI
implementations identified in theIntel® oneAPI Math Kernel Library Release Notes.
To link with ScaLAPACK, Cluster FFT, and/or Cluster Sparse Solver, use the following commands:

set lib =<path to MKL libraries>;<path to MPI libraries>;%lib%<linker> <files to link> [<MKL cluster library>] <BLACS><MKL core libraries><MPI
libraries>
where the placeholders stand for paths and libraries as explained in the following table:<path to MKL libraries><mkl directory>\lib\intel64. If you performed<path to MPI libraries>

Typically the lib subdirectory in the MPI
installation directory.

<linker>

One of icl, ifort, xilink.

<MKL cluster library>

One of libraries for ScaLAPACK or Cluster FFT listed
in Appendix C: Directory Structure in Detail. For
example, for the LP64 interface, it ismkl_scalapack_lp64.lib or
mkl_cdft_core.lib. Cluster Sparse Solver does
not require an additional computation library.

<BLACS>

The BLACS library corresponding to your ,programming interface (LP64 or ILP64), and MPI
version. These libraries are listed in Appendix C:
Directory Structure in Detail. For example, for the
LP64 interface, choose one of
mkl_blacs_intelmpi_lp64.lib, or
mkl_blacs_msmpi_lp64.lib in the case of static
linking and mkl_blacs_lp64_dll.lib in the caseof dynamic linking.

<MKL core libraries>Intel® oneAPI Math Kernel Library libraries otherthan libraries with ScaLAPACK, Cluster FFT, or
Cluster Sparse Solver.

the Scripts to Set Environment Variables Setting
Environment Variables step of the Getting Started
process, you do not need to add this directory to
the lib environment variable.

Tip
Use the Using the Link-line Advisor to quickly choose the appropriate set of <MKL cluster
Library>, <BLACS>, and <MKL core libraries>.

Intel MPI provides prepackaged scripts for its linkers to help you link using the respective linker. Therefore, if
you are using Intel MPI, the best way to link is to use the following commands:<path to Intel MPI binaries>\vars.bat
set lib = <path to MKL libraries>;%lib%<mpilinker><files to link> [<MKL cluster Library>] <BLACS><MKL core libraries>
where the placeholders that are not yet defined are explained in the following table:

919Intel® oneAPI Math Kernel Libraryfor Windows* Developer Guide<path to MPI binaries>

By default, the bin subdirectory in the MPI installation
directory.

<MPI linker>

mpicl or mpiifort

See Also
Linking Your Application with the Intel® oneAPI Math Kernel Library
Examples of Linking for Clusters

Determining the Number of OpenMP* Threads
The OpenMP* run-time library responds to the environment variable OMP_NUM_THREADS. Intel® oneAPI Math
Kernel Library also has other mechanisms to set the number of OpenMP threads, such as
theMKL_NUM_THREADS or MKL_DOMAIN_NUM_THREADS environment variables (see Using Additional Threading
Control).
Make sure that the relevant environment variables have the same and correct values on all the nodes. Intel®
oneAPI Math Kernel Library does not set the default number of OpenMP threads to one, but depends on the
OpenMP libraries used with the compiler to set the default number. For the threading layer based on the Intel
compiler (mkl_intel_thread.lib), this value is the number of CPUs according to the OS.
CautionAvoid over-prescribing the number of OpenMP threads, which may occur, for instance, when thenumber of MPI ranks per node and the number of OpenMP threads per node are both greater thanone. The number of MPI ranks per node multiplied by the number of OpenMP threads per node should
not exceed the number of hardware threads per node.
The OMP_NUM_THREADS environment variable is assumed in the discussion below.
Set OMP_NUM_THREADS so that the product of its value and the number of MPI ranks per node equals thenumber of real processors or cores of a node. If the Intel® Hyper-Threading Technology is enabled on thenode, use only half number of the processors that are visible on Windows OS.
Important
For Cluster Sparse Solver, set the number of OpenMP threads to a number greater than one because
the implementation of the solver only supports a multithreaded algorithm.

See Also
Setting Environment Variables on a Cluster

Using DLLs
All the needed DLLs must be visible on all the nodes at run time, and you should install Intel® oneAPI Math
Kernel Library (oneMKL) on each node of the cluster. You can use Remote Installation Services (RIS) provided
by Microsoft to remotely install the library on each of the nodes that are part of your cluster. The best way tomake the DLLs visible is to point to these libraries in thePATH environment variable. See Setting
Environment Variables on a Cluster on how to set the value of the PATH environment variable.
The ScaLAPACK DLLs in the <mkl directory>\redist\intel64directory use the MPI dispatchingmechanism. MPI dispatching is based on the MKL_BLACS_MPI environment variable. The BLACS DLL uses
MKL_BLACS_MPI for choosing the needed MPI libraries. The table below lists possible values of the variable.

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Value

Comment

INTELMPI

Default value. Intel MPI is used for message passing

MSMPI

Microsoft MPI is used for message passing

CUSTOM

Intel® oneAPI Math Kernel Library MPI wrappers built with a custom MPI are used formessage passing

If you are using a non-default MPI, assign the same appropriate value to MKL_BLACS_MPI on all nodes.

See Also
Setting Environment Variables on a Cluster
Notational Conventions

Setting Environment Variables on a Cluster
By default, when you call the MPI launch command mpiexec, the entire launching node environment ispassed to the MPI processes. However, if there are undefined variables or variables that are different fromwhat is stored in your environment, you can use -env or -genv options with mpiexec. Each of these options
take two arguments- the name and the value of the environment variable to be passed.

-genv NAME1 VALUE1 -genv NAME2 VALUE2-env NAME VALUE -genv
See these Intel MPI examples on how to set the value of OMP_NUM_THREADS explicitly:

mpiexec -genv OMP_NUM_THREADS 2 ....
mpiexec -n 1 -host first -env OMP_NUM_THREADS 2 test.exe : -n 2 -host second -env
OMP_NUM_THREADS 3 test.exe ....
See these Intel MPI examples on how to set the value of MKL_BLACS_MPI explicitly:

mpiexec -genv MKL_BLACS_MPI INTELMPI ....
mpiexec -n 1 -host first -env MKL_BLACS_MPI INTELMPI test.exe : -n 1 -host second -env
MKL_BLACS_MPI INTELMPI test.exe.
If you want to pass all environment variables by default and avoid passing these values explicitly, modify theuser or system environment variables on each Windows node. From the Start menu, select Settings >Control Panel > System > Advanced > Environment Variables.
If you are using Microsoft MPI, the ways of setting environment variables described above are also applicable
if the Microsoft Single Program Multiple Data (SPMD) process managers are running in a debug mode on all
nodes of the cluster. However, the best way to set environment variables is by using the Job Scheduler with
the Microsoft Management Console (MMC) and/or the Command Line Interface (CLI) to submit a job andpass environment variables. For more information about MMC and CLI, see the Microsoft Help and Support
page at the Microsoft Web site (http://www.microsoft.com/).

Interaction with the Message-passing Interface
To improve performance of cluster applications, it is critical for Intel® oneAPI Math Kernel Library to use theoptimal number of threads, as well as the correct thread affinity. Usually, the optimal number is the numberof available cores per node divided by the number of MPI processes per node. You can set the number of
threads using one of the available methods, described inTechniques to Set the Number of Threads.
If the number of threads is not set, Intel® oneAPI Math Kernel Library checks whether it runs under MPI
provided by the Intel® MPI Library. If this is true, the following environment variables define Intel® oneAPI
Math Kernel Library threading behavior:

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Intel® oneAPI Math Kernel Libraryfor Windows* Developer Guide

I_MPI_THREAD_LEVELMKL_MPI_PPN
I_MPI_NUMBER_OF_MPI_PROCESSES_PER_NODE
I_MPI_PIN_MAPPINGOMPI_COMM_WORLD_LOCAL_SIZE
MPI_LOCALNRANKS

The threading behavior depends on the value of I_MPI_THREAD_LEVEL as follows:•

0 or undefined.

•Intel® oneAPI Math Kernel Library considers that thread support level of Intel MPI Library
isMPI_THREAD_SINGLE and defaults to sequential execution.
1, 2, or 3.
This value determines Intel® oneAPI Math Kernel Library conclusion of the thread support level:•••

1 - MPI_THREAD_FUNNELED
2 - MPI_THREAD_SERIALIZED
3 - MPI_THREAD_MULTIPLE

In all these cases, Intel® oneAPI Math Kernel Library determines the number of MPI processes per nodeusing the other environment variables listed and defaults to the number of threads equal to the number ofavailable cores per node divided by the number of MPI processes per node.
ImportantInstead of relying on the discussed implicit settings, explicitly set the number of threads for Intel®
oneAPI Math Kernel Library.

Product and Performance Information
Performance varies by use, configuration and other factors. Learn more at www.Intel.com/
PerformanceIndex.
Notice revision #20201201

See Also
Managing Multi-core PerformanceIntel® Software Documentation Library for more information on Intel MPI Libraryfor more information on Intel MPI Library

Using a Custom Message-Passing Interface
While different message-passing interface (MPI) libraries are compatible at the application programming
interface (API) level, they are often incompatible at the application binary interface (ABI) level. Therefore,
Intel® oneAPI Math Kernel Library provides a set of prebuilt BLACS libraries that support certain MPI libraries,
but this, however, does not enable use of Intel® oneAPI Math Kernel Library with other MPI libraries. To fillthis gap, Intel® oneAPI Math Kernel Library also includes the MKL MPI wrapper, which provides an MPIindependent ABI to Intel® oneAPI Math Kernel Library. The adaptor is provided as source code. To use Intel®
oneAPI Math Kernel Library with an MPI library that is not supported by default, you can use the adapter tobuild custom static or dynamic BLACS libraries and use them similarly to the prebuilt libraries.

Building a Custom BLACS Library
The MKL MPI wrapper is located in the <mkl directory>\interfaces\mklmpi directory.
To build a custom BLACS library, from the above directory run the nmake command.
For example: the command

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9

nmake libintel64
builds a static custom BLACS library mkl_blacs_custom_lp64.lib using the default MPI compiler on yoursystem. Look into the <mkl directory>\interfaces\mklmpi\makefile for targets and variables thatdefine how to build the custom library. In particular, you can specify the compiler through the MPICC variable.
For more control over the building process, refer to the documentation available through the command

nmake help

Using a Custom BLACS Library
In the case of static linking, use custom BLACS libraries exactly the same way as you use the prebuilt BLACS
libraries, but pass the custom library to the linker. For example, instead of passing the
mkl_blacs_intelmpi_lp64.lib static library, pass mkl_blacs_custom_lp64.lib.
To use a dynamic custom BLACS library:1.
2.

Link your application the same way as when you use the prebuilt BLACS library.
Call the mkl_set_mpi support function or set the MKL_BLACS_MPI environment variable to one of thefollowing values:•

CUSTOM
to load a custom library with the default name mkl_blacs_custom_lp64.dll or
mkl_blacs_custom_ilp64.dll, depending on whether the BLACS interface linked against yourapplication is LP64 or ILP64.

•<dll_name>to load the specified BLACS DLL.
NOTEIntel® oneAPI Math Kernel Library looks for the specified DLL either in the directory with Intel® oneAPI
Math Kernel Library dynamic libraries or in the directory with the application executable.

For a description of the mkl_set_mpi function, see the Intel® oneAPI Math Kernel Library Developer
Reference.

See Also
Linking with Intel® oneAPI Math Kernel Library Cluster Software

Examples of Linking for Clusters
This section provides examples of linking with ScaLAPACK, Cluster FFT, and Cluster Sparse Solver.
Note that a binary linked with the Intel® oneAPI Math Kernel Library cluster function domains runs the same
way as any other MPI application (refer to the documentation that comes with your MPI implementation).
For further linking examples, see the support website for Intel products at https://software.intel.com/
content/www/us/en/develop/support.html.

See Also
Directory Structure in Detail

Examples for Linking a C Application
These examples illustrate linking of an application under the following conditions:

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Main module is in C.
Intel MPI is installed in C:\Program Files(x86)\Intel\oneAPI\mpi. Instructions on how to installIntel MPI can be found on the Get Started with Intel® MPI Library for Windows* OS and Intel® oneAPI
Toolkits Installation Guide for Windows* pages.
You are using the Intel® C++ Compiler.
Intel® oneAPI Math Kernel Library functions use LP64 interfaces.

To set the Intel MPI environment, you must run the <path to Intel MPI binaries>\vars.bat script.
To link with ScaLAPACK for a cluster of Intel® 64 architecture based systems, set the environment variable
and use the link line as follows:

set lib=<mkl directory>\lib\intel64_win;%lib%
icl <user files to link> mkl_scalapack_lp64.lib blacs_intelmpi_lp64.lib
mkl_intel_lp64.lib mkl_intel_thread.lib mkl_core.lib libiomp5md.lib impi.lib
bufferoverflowu.lib
To link with Cluster FFT for a cluster of Intel® 64 architecture based systems, set the environment variable
and use the link line as follows:

set lib=<mkl directory>\lib\intel64_win;%lib%
icl <user files to link> mkl_cdft_core.lib mkl_blacs_intelmpi_lp64.lib
mkl_intel_lp64.lib mkl_intel_thread.lib mkl_core.lib libiomp5md.lib impi.lib
bufferoverflowu.lib
To link with Cluster Sparse Solver for a cluster of Intel® 64 architecture based systems, set the environment
variable and use the link line as follows:

set lib=<mkl directory>\lib\intel64_win;%lib%
icl <user files to link> mkl_blacs_intelmpi_lp64.lib mkl_intel_lp64.lib
mkl_intel_thread.lib mkl_core.lib libiomp5md.lib impi.lib bufferoverflowu.lib

See Also
Linking with oneMKL Cluster Software
Using the Link-line Advisor
Linking with System Libraries

Examples for Linking a Fortran Application
These examples illustrate linking of an application under the following conditions:
•
•

•
•

Main module is in Fortran.
Intel MPI is installed in C:\Program Files(x86)\Intel\oneAPI\mpi. Instructions on how to installIntel MPI can be found on the Get Started with Intel® MPI Library for Windows* OS and Intel® oneAPI
Toolkits Installation Guide for Windows* pages.
You are using the Intel® Fortran Compiler.
Intel® oneAPI Math Kernel Library functions use LP64 interfaces.

To set the Intel MPI environment, you must run the <path to Intel MPI binaries>\vars.bat script.
To link with ScaLAPACK for a cluster of Intel® 64 architecture based systems, set the environment variable
and use the link line as follows:

set lib=<mkl directory>\lib\intel64_win;%lib%
ifort <user files to link> mkl_scalapack_lp64.lib mkl_blacs_intelmpi_lp64.lib
mkl_intel_lp64.lib mkl_intel_thread.lib mkl_core.lib libiomp5md.lib impi.lib
bufferoverflowu.lib

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To link with Cluster FFT for a cluster of Intel® 64 architecture based systems, set the environment variable
and use the link line as follows:

set lib=<mkl directory>\lib\intel64_win;%lib%
ifort <user files to link> mkl_cdft_core.lib mkl_blacs_intelmpi_lp64.lib
mkl_intel_lp64.lib mkl_intel_thread.lib mkl_core.lib libiomp5md.lib impi.lib
bufferoverflowu.lib
To link with Cluster Sparse Solver for a cluster of Intel® 64 architecture based systems, set the environment
variable and use the link line as follows:

set lib=<mkl directory>\lib\intel64_win;%lib%
ifort <user files to link> mkl_blacs_intelmpi_lp64.lib mkl_intel_lp64.lib
mkl_intel_thread.lib mkl_core.lib libiomp5md.lib impi.lib bufferoverflowu.lib

See Also
Linking with oneMKL Cluster Software
Using the Link-line Advisor
Linking with System Libraries

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Managing Behavior of the Intel®
oneAPI Math Kernel Library withEnvironment Variables

10

See Also
Intel® oneAPI Math Kernel Library-specific Environment Variables for Threading Control
Specifying the Code Branches

for how to use an environment variable to specify the code branch for Conditional Numerical Reproducibility

Using Intel® oneAPI Math Kernel Library Verbose Modefor how to use an environment variable to set the verbose mode

Managing Behavior of Function Domains with Environment
Variables
Setting the Default Mode of Vector Math with an Environment Variable
Intel® oneAPI Math Kernel Library (oneMKL) enables overriding the default setting of the Vector Mathematics(VM) global mode using theMKL_VML_MODE environment variable.
Because the mode is set or can be changed in different ways, their precedence determines the actual mode
used. The settings and function calls that set or change the VM mode are listed below, with the precedence
growing from lowest to highest:
1.
2.
3.
4.

The default setting
The MKL_VML_MODE environment variable
A call vmlSetMode function
A call to any VM function other than a service function

For more details, see the Vector Mathematical Functions section in the Intel® oneAPI Math Kernel Library
Developer Reference and the description of the vmlSetMode function in particular.
To set the MKL_VML_MODE environment variable, use the following command:set MKL_VML_MODE=<mode-string>
In this command, <mode-string> controls error handling behavior and computation accuracy, consists of
one or several comma-separated values of the mode parameter listed in the table below, and meets theserequirements:•••

Not more than one accuracy control value is permitted
Any combination of error control values except VML_ERRMODE_DEFAULT is permitted
No denormalized numbers control values are permitted

Values of the mode Parameter
Value of mode

Description

Accuracy Control

VML_HA
VML_LA
VML_EP
Denormalized Numbers Handling Control

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low accuracy versions of VM functions
enhanced performance accuracy versions of VM functions

Managing Behavior of the Intel® oneAPI Math Kernel Library with Environment Variables

Value of mode

Description

VML_FTZDAZ_ON
VML_FTZDAZ_OFF
VML_FTZDAZ_CURRENT

Faster processing of denormalized inputs is enabled.

10

Faster processing of denormalized inputs is disabled.
Keep the current CPU settings for denormalized inputs.

Error Mode Control

VML_ERRMODE_IGNORE
VML_ERRMODE_NOERR
VML_ERRMODE_STDERR
VML_ERRMODE_EXCEPTVML_ERRMODE_CALLBACKVML_ERRMODE_DEFAULT

On computation error, VM Error status is updated, but otherwise noaction is set. Cannot be combined with other VML_ERRMODE
settings.
On computation error, VM Error status is not updated and no action isset. Cannot be combined with other VML_ERRMODE settings.
On error, the error text information is written to stderr.
On error, an exception is raised.
On error, an additional error handler function is called.
On error, an exception is raised and an additional error handler
function is called.

This command provides an example of valid settings for the MKL_VML_MODE environment variable:

set MKL_VML_MODE=VML_LA,VML_ERRMODE_ERRNO,VML_ERRMODE_STDERR
NOTE
VM ignores the MKL_VML_MODE environment variable in the case of incorrect or misspelled settings ofmode.

Managing Performance of the Cluster Fourier Transform Functions
Performance of Intel® oneAPI Math Kernel Library Cluster FFT (CFFT) in different applications mainly depends
on the cluster configuration, performance of message-passing interface (MPI) communications, andconfiguration of the run. Note that MPI communications usually take approximately 70% of the overall CFFT
compute time.For more flexibility of control over time-consuming aspects of CFFT algorithms, Intel® oneAPI
Math Kernel Library provides theMKL_CDFT environment variable to set special values that affect CFFT
performance. To improve performance of your application that intensively calls CFFT, you can use theenvironment variable to set optimal values for you cluster, application, MPI, and so on.
The MKL_CDFT environment variable has the following syntax, explained in the table below:

MKL_CDFT=option1[=value1],option2[=value2],…,optionN[=valueN]
Important
While this table explains the settings that usually improve performance under certain conditions, theactual performance highly depends on the configuration of your cluster. Therefore, experiment with thelisted values to speed up your computations.

OptionPossible
ValuesDescription

alltoallv

0 (default)

Configures CFFT to use the standard MPI_Alltoallv function to
perform global transpositions.

1

Configures CFFT to use a series of calls to MPI_Isend and MPI_Irecv
instead of the MPI_Alltoallv function.

4

Configures CFFT to merge global transposition with data movementsin the local memory. CFFT performs global transpositions by calling
MPI_Isend and MPI_Irecv in this case.

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OptionPossible
ValuesDescription
Use this value in a hybrid case (MPI + OpenMP), especially when thenumber of processes per node equals one.

wo_omatcopy

0

Configures CFFT to perform local FFT and local transpositions
separately.
CFFT usually performs faster with this value than with wo_omatcopy= 1 if the configuration parameter DFTI_TRANSPOSE has the value of
DFTI_ALLOW. See the Intel® oneAPI Math Kernel Library Developer
Reference for details.

1

Configures CFFT to merge local FFT calls with local transpositions.
CFFT usually performs faster with this value than with wo_omatcopy= 0 if DFTI_TRANSPOSE has the value of DFTI_NONE.

enable_soi-1 (default)

Enables CFFT to decide which of the two above values to use
depending on the value of DFTI_TRANSPOSE.

Not applicable

A flag that enables low-communication Segment Of Interest FFT (SOI
FFT) algorithm for one-dimensional complex-to-complex CFFT, which
requires fewer MPI communications than the standard nine-step (orsix-step) algorithm.
CautionWhile using fewer MPI communications, the SOI FFT algorithm incurs aminor loss of precision (about one decimal digit).

The following example illustrates usage of the environment variable:

set MKL_CDFT=wo_omatcopy=1,alltoallv=4,enable_soi
mpirun –ppn 2 –n 16 mkl_cdft_app.exe
Product and Performance Information
Performance varies by use, configuration and other factors. Learn more at www.Intel.com/
PerformanceIndex.
Notice revision #20201201

Managing Invalid Input Checking in LAPACKE Functions
The high-level interface includes an optional, on by default, NaN check on all matrix inputs before calling any
LAPACK routine. This option affects all routines. If an input matrix contains any NaNs, the input parametercorresponding to this matrix is flagged with a return value error. For example, if the fifth parameter is foundto contain a NaN, the routine returns the value, -5. The middle-level interface does not contain the NaN
check.
NaN checking on matrix input can be expensive. By default, NaN checking is turned on. LAPACKE provides away to set it through the environment variable:••

Setting environment variable LAPACKE_NANCHECK to 0 turns OFF NaN-checking
Setting environment variable LAPACKE_NANCHECK to 1 turns ON NaN-checking

The other way is the call the LAPACKE_set_nancheck function; see the Developer Reference for C's LAPACK
Auxiliary Routines section for more information.
Note that the NaN-checking flag value set by the call to LAPACKE_set_nancheck always has higher priority
than the environment variable, LAPACKE_NANCHECK.

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Instruction Set Specific Dispatching on Intel® Architectures
Intel® oneAPI Math Kernel Library automatically queries and then dispatches the code path supported on yourIntel® processor to the optimal instruction set architecture (ISA) by default. The MKL_ENABLE_INSTRUCTIONS
environment variable or the mkl_enable_instructions support function enables you to dispatch to an ISAspecific code path of your choice. For example, you can run the Intel® Advanced Vector Extensions (Intel®
AVX) code path on an Intel processor based on Intel® Advanced Vector Extensions 2 (Intel® AVX2). Thisfeature is not available on non-Intel processors.
In some cases Intel® oneAPI Math Kernel Library also provides support for upcoming architectures ahead of
hardware availability, but the library does not automatically dispatch the code path specific to an upcoming
ISA by default. If for your exploratory work you need to enable an ISA for an Intel processor that is not yet
released or if you are working in a simulated environment, you can use theMKL_ENABLE_INSTRUCTIONS
environment variable or mkl_enable_instructions support function.
The following table lists possible values of MKL_ENABLE_INSTRUCTIONS alongside the corresponding ISA
supported by a given processor. MKL_ENABLE_INSTRUCTIONSdispatches to the default ISA if the ISA
requested is not supported on the particular Intel processor. For example, if you request to run the Intel
AVX512 code path on a processor based on Intel AVX2, Intel® oneAPI Math Kernel Library runs the Intel AVX2
code path. The table also explains whether the ISA is dispatched by default on the processor that supports
this ISA.
Value of

ISA

Dispatched by
Default

AVX512

Intel® Advanced Vector Extensions (Intel®
AVX-512) for systems based on Intel® Xeon®
processors

Yes

AVX512_E1

Intel® Advanced Vector Extensions (Intel®
AVX-512) with support for Vector Neural
Network Instructions.

Yes

AVX512_E2

ICX: Intel® Advanced Vector Extensions (Intel®
AVX-512) enabled processors.

Yes

AVX512_E3Intel® Advanced Vector Extensions 512 (Intel®
AVX-512) with support of Vector Neural
Network Instructions supporting BF16 enabled
processors.

Yes

AVX512_E4

Intel® Advanced Vector Extensions 512 (Intel®
AVX-512) with Intel® Deep Learning Boost(Intel® DL Boost) and bfloat16 support andIntel® Advanced Matrix Extensions (Intel® AMX)
with bfloat16 and 8-bit integer support.

Yes

AVX2

Intel® AVX2

Yes

AVX2_E1

Intel® Advanced Vector Extensions 2 (Intel®
AVX2) with support for Intel® Deep Learning
Boost (Intel® DL Boost).

Yes

AVX

Intel® AVX

Yes

MKL_ENABLE_INSTRUCTIONS

For more details about the mkl_enable_instructions function, including the argument values, see theIntel® oneAPI Math Kernel Library Developer Reference.
For example:

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To turn on automatic CPU-based dispatching of Intel AVX-512 with support of Intel DL Boost, bfloat16,
Intel AMX with bfloat16 and 8-bit integer, and FP16 instruction, do one of the following:
•

Call

•

Set the environment variable:

mkl_enable_instructions(AVX512_E4)

set MKL_ENABLE_INSTRUCTIONS=AVX512_E4

To configure the library not to dispatch more recent architectures than Intel AVX2, do one of thefollowing:
•
•

Call

mkl_enable_instructions(MKL_ENABLE_AVX2)
Set the environment variable:

set MKL_ENABLE_INSTRUCTIONS=AVX2
NOTE
Settings specified by the mkl_enable_instructions function take precedence over the settings
specified by the MKL_ENABLE_INSTRUCTIONS environment variable.

Product and Performance Information
Performance varies by use, configuration and other factors. Learn more at www.Intel.com/
PerformanceIndex.
Notice revision #20201201

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Programming with Intel® Math
Kernel Library in Integrated
Development Environments
(IDE)

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11

Configuring Your Integrated Development Environment to
Link with Intel® oneAPI Math Kernel Library
See these Knowledge Base articles for how to configure your Integrated Development Environment for linking
with Intel® oneAPI Math Kernel Library:••

Compiling and Linking Intel® oneAPI Math Kernel Library with Microsoft* Visual C/C++* (http://
software.intel.com/en-us/articles/intel-math-kernel-library-intel-mkl-compiling-and-linking-withmicrosoft-visual-cc)
How to Build an Intel® oneAPI Math Kernel Library Application with Intel® Visual Fortran Compiler (http://
software.intel.com/en-us/articles/how-to-build-mkl-application-in-intel-visual-fotran-msvc2005)

Configuring the Microsoft Visual C/C++* Development System to Link with Intel® MKL
Steps for configuring Microsoft Visual C/C++* development system for linking with Intel® oneAPI Math Kernel
Library (oneMKL) depend on whether you installed the C++ Integration(s) in Microsoft Visual Studio*
component of the Intel® Parallel Studio XE Composer Edition:••

If you installed the integration component, see Automatically Linking Your Microsoft Visual C/C++*
Project with Intel® MKL.
If you did not install the integration component or need more control over Intel® oneAPI Math Kernel
Library libraries to link, you can configure the Microsoft Visual C++* development system by performing
the following steps. Though some versions of the Visual C++* development system may vary slightly inthe menu items mentioned below, the fundamental configuring steps are applicable to all these versions.
1. In Solution Explorer, right-click your project and click Properties
2. Select Configuration Properties > VC++ Directories
3. Select Include Directories. Add the directory for the Intel® oneAPI Math Kernel Library include files,
that is,<mkl directory>\include
4. Select Library Directories. Add architecture-specific directories for Intel® oneAPI Math Kernel Libraryand OpenMP* libraries,
for example: <mkl directory>\lib\ia32 and <compiler directory>\windows\compiler\lib\ia32_win

5. Select Executable Directories. Add architecture-specific directories with dynamic-link libraries:•

For OpenMP* support, for example: <compiler directory>\windows\redist\ia32_win•

For Intel® oneAPI Math Kernel Library (only if you link dynamically), for example:<mkl directory>\compiler\redist\ia32

6. Select Configuration Properties > Custom Build Setup > Additional Dependencies. Add thelibraries required, for example, mkl_intel_c.lib mkl_intel_thread.lib mkl_core.lib

libiomp5md.lib

See Also
Intel® Software Documentation Library for the documentation for Intel Parallel Studio XE
Composer Edition
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for the documentation for Intel Parallel Studio XE Composer Edition

Linking in Detail
Notational Conventions

Configuring Intel® Visual Fortran to Link with Intel MKL
Steps for configuring Intel® Visual Fortran for linking with Intel® oneAPI Math Kernel Library (oneMKL) depend
on whether you installed the Visual Fortran Integration(s) in Microsoft Visual Studio* component of the Intel®
Parallel Studio XE Composer Edition:••

If you installed the integration component, see Automatically Linking Your Intel® Visual Fortran Projectwith Intel® MKL.
If you did not install the integration component or need more control over Intel® oneAPI Math Kernel
Library libraries to link, you can configure your project as follows:
1. Select Project > Properties > Linker > General > Additional Library Directories. Addarchitecture-specific directories for Intel® oneAPI Math Kernel Library and OpenMP* libraries,
for example: <mkl directory>\lib\ia32 and <parent directory>\compiler\lib\ia32
2. Select Project > Properties > Linker > Input > Additional Dependencies. Insert names of therequired libraries, for example: mkl_intel_c.lib mkl_intel_thread.lib mkl_core.lib

libiomp5md.lib

3. Select Project > Properties > Debugging > Environment. Add architecture-specific paths todynamic-link libraries:••

For OpenMP* support; for example: enter PATH=%PATH%;<compiler directory>\windows\redist\ia32_win\compiler

For Intel® oneAPI Math Kernel Library (only if you link dynamically); for example: enter PATH=%PATH%;<mkl directory>\redist\ia32

See Also
Intel® Software Documentation Library for the documentation for Intel Parallel Studio XE
Composer Edition
for the documentation for Intel Parallel Studio XE Composer Edition

Notational Conventions

Getting Assistance for Programming in the Microsoft Visual
Studio* IDEUsing Context-Sensitive Help
You can get context-sensitive help when typing your code in the Visual Studio* IDE Code Editor. To open thehelp topic describing an Intel® oneAPI Math Kernel Library function called in your code, select the function
name and press F1. The topic with the function description opens in the Microsoft Help Viewer or your Web
browser depending on the Visual Studio IDE Help settings.

Using the IntelliSense* Capability
IntelliSense is a set of native Visual Studio*(VS) IDE features that make language references easily
accessible.
The user programming with Intel® oneAPI Math Kernel Library in the VS Code Editor can employ twoIntelliSense features: Parameter Info and Complete Word.
Both features use header files. Therefore, to benefit from IntelliSense, make sure the path to the include files
is specified in the VS or solution settings. For example, see Configuring the Microsoft Visual C/C++*
Development System to Link with Intel® MKL on how to do this.

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Parameter Info
The Parameter Info feature displays the parameter list for a function to give information on the number andtypes of parameters. This feature requires adding the includestatement with the appropriate Intel® oneAPI
Math Kernel Library header file to your code.
To get the list of parameters of a function specified in the header file,
1.
2.

Type the function name.
Type the opening parenthesis.

This brings up the tooltip with the list of the function parameters:

Complete Word
For a software library, the Complete Word feature types or prompts for the rest of the name defined in theheader file once you type the first few characters of the name in your code. This feature requires adding theincludestatement with the appropriate Intel® oneAPI Math Kernel Library header file to your code.
To complete the name of the function or named constant specified in the header file,
1.
2.

3.

Type the first few characters of the name.
Press Alt+RIGHT ARROW or Ctrl+SPACEBAR.
If you have typed enough characters to disambiguate the name, the rest of the name is typed
automatically. Otherwise, a pop-up list appears with the names specified in the header file
Select the name from the list, if needed.

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Intel® oneAPI Math Kernel
Library Benchmarks

12

12

Product and Performance Information
Performance varies by use, configuration and other factors. Learn more at www.Intel.com/
PerformanceIndex.
Notice revision #20201201

Intel Optimized LINPACK Benchmark for Windows*
Intel Optimized LINPACK Benchmark for Windows* is a generalization of the LINPACK 1000 benchmark. It
solves a dense (real*8) system of linear equations (Ax=b), measures the amount of time it takes to factor
and solve the system, converts that time into a performance rate, and tests the results for accuracy. The
generalization is in the number of equations (N) it can solve, which is not limited to 1000. It uses partial
pivoting to assure the accuracy of the results.
Do not use this benchmark to report LINPACK 100 performance because that is a compiled-code only
benchmark. This is a shared-memory (SMP) implementation which runs on a single platform. Do not confuse
this benchmark with:••Intel® Distribution for LINPACK* Benchmark, which is a distributed memory version of the same
benchmark.
LINPACK, the library, which has been expanded upon by the LAPACK library.

Intel provides optimized versions of the LINPACK benchmarks to help you obtain high LINPACK benchmark
results on your genuine Intel processor systems more easily than with the High Performance Linpack (HPL)
benchmark.
Additional information on this software, as well as on other Intel® software performance products, is available
at https://software.intel.com/content/www/us/en/develop/tools.html.

Acknowledgement
This product includes software developed at the University of Tennessee, Knoxville, Innovative Computing
Laboratories.

Contents of the Intel® Optimized LINPACK Benchmark
The Intel Optimized LINPACK Benchmark for Windows* contains the following files, located in thebenchmarks\linpack\subdirectory of the Intel® oneAPI Math Kernel Library (oneMKL) directory:
File in benchmarks\linpack\Description

linpack_xeon32.exe

The 32-bit program executable for a system based on Intel®
Xeon® processor or Intel® Xeon® processor MP with or withoutIntel® Streaming SIMD Extensions 3 (SSE3).

linpack_xeon64.exe

The 64-bit program executable for a system with Intel Xeon
processor using Intel® 64 architecture.

runme_xeon32.bat

A sample shell script for executing a pre-determined problem set
for linpack_xeon32.exe.

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File in benchmarks\linpack\Description

runme_xeon64.bat

A sample shell script for executing a pre-determined problem set
for linpack_xeon64.exe.

lininput_xeon32

Input file for a pre-determined problem for the runme_xeon32
script.

lininput_xeon64

Input file for a pre-determined problem for the runme_xeon64
script.

help.lpk

Simple help file.

xhelp.lpk

Extended help file.

These files are not available immediately after installation and appear as a result of execution of an
appropriate runme script.

win_xeon32.txt

Result of the runme_xeon32 script execution.

win_xeon64.txt

Result of the runme_xeon64 script execution.

See Also
High-level Directory Structure

Running the Software
To obtain results for the pre-determined sample problem sizes on a given system, type:runme_xeon32.batrunme_xeon64.bat
To run the software for other problem sizes, see the extended help included with the program. You can view
extended help by running the program executable with the -e option:

linpack_xeon32.exe-e
linpack_xeon64.exe -e
The pre-defined data input files lininput_xeon32 and lininput_xeon64 are examples. Different systems
have different numbers of processors or amounts of memory and therefore require new input files. The
extended help can give insight into proper ways to change the sample input files.
Each input file requires the following minimum amount of memory:lininput_xeon32

2 GB

lininput_xeon64

16 GB

If the system has less memory than the above sample data input requires, you may need to edit or create
your own data input files, as explained in the extended help.
The Intel Optimized LINPACK Benchmark determines the optimal number of OpenMP threads to use. To run adifferent number, you can set the OMP_NUM_THREADS or MKL_NUM_THREADS environment variable inside asample script. If you run the Intel Optimized LINPACK Benchmark without setting the number of threads, it
defaults to the number of physical cores.
Product and Performance Information
Performance varies by use, configuration and other factors. Learn more at www.Intel.com/
PerformanceIndex.

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Product and Performance Information
Notice revision #20201201

Known Limitations of the Intel® Optimized LINPACK Benchmark
The following limitations are known for the Intel Optimized LINPACK Benchmark for Windows*:•••

Intel Optimized LINPACK Benchmark supports only OpenMP threadingIntel Optimized LINPACK Benchmark is threaded to effectively use multiple processors. So, in multiprocessor systems, best performance will be obtained with the Intel® Hyper-Threading Technology turnedoff, which ensures that the operating system assigns threads to physical processors only.
If an incomplete data input file is given, the binaries may either hang or fault. See the sample data input
files and/or the extended help for insight into creating a correct data input file.

Intel® Distribution for LINPACK* Benchmark
Overview of the Intel® Distribution for LINPACK* Benchmark
The Intel® Distribution for LINPACK* Benchmark is based on modifications and additions to High-Performance
LINPACK (HPL) (http://www.netlib.org/benchmark/hpl/) from Innovative Computing Laboratories (ICL) at the
University of Tennessee, Knoxville. The Intel® Distribution for LINPACK Benchmark can be used for TOP500
runs (see http://www.top500.org) and for benchmarking your cluster. To use the benchmark you need to be
familiar with HPL usage. The Intel® Distribution for LINPACK Benchmark provides some enhancements
designed to make the HPL usage more convenient and to use Intel® Message-Passing Interface (MPI) settings
to improve performance.
The Intel® Distribution for LINPACK Benchmark measures the amount of time it takes to factor and solve a
random dense system of linear equations (Ax=b) in real*8 precision, converts that time into a performance
rate, and tests the results for accuracy. The benchmark uses random number generation and full row pivoting
to ensure the accuracy of the results.
Intel provides optimized versions of the LINPACK benchmarks to help you obtain high LINPACK benchmark
results on your systems based on genuine Intel processors more easily than with the standard HPL
benchmark. The prebuilt binaries require Intel® MPI library be installed on the cluster. The run-time version ofIntel MPI library is free and can be downloaded from https://www.software.intel.com/content/www/us/en/
develop/tools.html .
The Intel package includes software developed at the University of Tennessee, Knoxville, ICL, and neither the
University nor ICL endorse or promote this product. Although HPL is redistributable under certain conditions,this particular package is subject to the Intel® oneAPI Math Kernel Library (oneMKL) license.
Intel® oneAPI Math Kernel Library provides prebuilt binaries that are linked against Intel MPI libraries either
statically or dynamically. In addition, binaries linked with a customized MPI implementation can be createdusing the Intel® oneAPI Math Kernel Library MPI wrappers.
NOTE
Performance of statically and dynamically linked prebuilt binaries may be different. The performance ofboth depends on the version of Intel MPI you are using. You can build binaries statically or dynamicallylinked against a particular version of Intel MPI by yourself.
HPL code is homogeneous by nature: it requires that each MPI process runs in an environment with similar
CPU and memory constraints. The Intel® Distribution for LINPACK Benchmark supports heterogeneity,
meaning that the data distribution can be balanced to the performance requirements of each node, provided
that there is enough memory on that node to support additional work. For information on how to configureIntel® oneAPI Math Kernel Library to use the internode heterogeneity, seeHeterogeneous Support in theIntel® Distribution for LINPACK Benchmark.

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Contents of the Intel® Distribution for LINPACK* Benchmark
The Intel® Distribution for LINPACK Benchmark includes prebuilt binaries linked with Intel® MPI library. For acustomized MPI implementation, tools are also included to build a binary using Intel® oneAPI Math Kernel
Library MPI wrappers. All the files are located in the.\benchmarks\mp_linpack\subdirectory of the Intel®
oneAPI Math Kernel Library directory.
File in <mkl directory>\benchmarks

Contents\mp_linpack\
COPYRIGHT

Original Netlib HPL copyright document.

readme.txt

Information about the files provided.

Prebuilt executables for performance testing

xhpl_intel64_dynamic.exe

Prebuilt binary for the Intel® 64 architecture dynamically linked
against Intel MPI library‡.

Run scripts and an input file example

runme_intel64_dynamic.bat

Sample run script for the Intel® 64 architecture and binary
dynamically linked against Intel MPI library.

runme_intel64_prv.bat

Script that sets HPL environment variables. It is called byrunme_intel64_dynamic.bat.

HPL.dat

Example of an HPL configuration file.

Prebuilt libraries and utilities for building with a customized MPI implementation

libhpl_intel64.lib

Library file required to build Intel® Distribution for LINPACK
Benchmark for the Intel® 64 architecture with a customized
MPI implementation.

HPL_main.c

Source code required to build Intel® Distribution for LINPACK
Benchmark for the Intel® 64 architecture with a customized
MPI implementation.

build.bat

Build script for creating Intel® Distribution for LINPACK
Benchmark for the Intel® 64 architecture with a customized
MPI implementation.

Utilities for building Netlib HPL from source code

Make.Windows_Intel64

Makefile for building Netlib HPL from source code.

‡For a list of supported versions of the Intel MPI Library, see system requirements in the Intel® oneAPI Math

Kernel Library Release Notes.

See Also
High-level Directory Structure

Building the Intel® Distribution for LINPACK* Benchmark for a Customized MPI
Implementation
The Intel® Distribution for LINPACK Benchmark contains a sample build script build.bat. If you are using acustomized MPI implementation, this script builds a binary using Intel® oneAPI Math Kernel Library MPI
wrappers. To build the binary, follow these steps:
1.

Specify the location of Intel® oneAPI Math Kernel Library to be used (MKLROOT)

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2.
3.

12

Set up your MPI environment
Run the script build.bat

See Also
Contents of the Intel® Distribution for LINPACK Benchmark

Building the Netlib HPL from Source CodeThe source code for Intel® Distribution for LINPACK* Benchmark is not provided. However, you can downloadreference Netlib HPL source code from http://www.netlib.org/benchmark/hpl/ . To build the HPL:
1.
2.
3.
4.
5.

Download and extract the source code.
Copy the makefile <mkl directory>\benchmarks\mp_linpack\Make.Windows_Intel64 to your HPL
directory
Edit Make.Windows_Intel64 as appropriate
Build the HPL binary:$> nmake -f Make.Windows_Intel64

Check that the built binary is available in the current directory.
NOTE
The Intel® Distribution for LINPACK Benchmark may contain additional optimizations compared to thereference Netlib HPL implementation.

Product and Performance Information
Performance varies by use, configuration and other factors. Learn more at www.Intel.com/
PerformanceIndex.
Notice revision #20201201

See Also
Contents of the Intel® Distribution for LINPACK Benchmark

Configuring Parameters
The most significant parameters in HPL.dat are P, Q, NB, and N. Specify them as follows:•

P and Q - the number of rows and columns in the process grid, respectively.
P*Q must be the number of MPI processes that HPL is using.

•Choose P≤Q.
NB - the block size of the data distribution.
The table below shows recommended values of NB for different Intel® processors:
Processor

NBIntel® Xeon® Processor X56*/E56*/E7-*/E7*/X7* (codenamed Nehalem or Westmere)

256

Intel Xeon Processor E26*/E26* v2 (codenamed Sandy Bridge or Ivy Bridge)

256Intel Xeon Processor E26* v3/E26* v4 (codenamed Haswell or Broadwell)

192

Intel® Core™ i3/i5/i7-6* Processor (codenamed Skylake Client)Intel® Xeon Phi™ Processor 72* (codenamed Knights Landing)

192
336

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Processor

NBIntel Xeon Processor supporting Intel® Advanced Vector Extensions 512 (Intel® AVX-512)
instructions (codenamed Skylake Server)

384•

N - the problem size:••

For homogeneous runs, choose N divisible by NB*LCM(P,Q), where LCM is the least common multipleof the two numbers.
For heterogeneous runs, see Heterogeneous Support in the Intel® Distribution for LINPACK*
Benchmark for how to choose N.
NOTE
Increasing N usually increases performance, but the size of N is bounded by memory. In general, you
can compute the memory required to store the matrix (which does not count internal buffers) as
8*N*N/(P*Q) bytes, where N is the problem size and P and Q are the process grids in HPL.dat. A
general rule of thumb is to choose a problem size that fills 80% of memory.

Ease-of-use Command-line Parameters
The Intel® Distribution for LINPACK* Benchmark supports command-line parameters for HPL that help you to
avoid making small changes in the HPL.dat input file every time you do a new run.
Placeholders in this command line illustrate these parameters:

xhpl.exe -n <problem size> -m <memory size in Mbytes> -b <block size> -p <grid row
dimn> -q <grid column dimn>
You can also use command-line parameters with the sample runme script. For example:

runme_intel64_dynamic.bat -m <memory size in Mbytes> -b <block size> -p <grid row dimn>-q <grid column dimn>
For more command-line parameters, see Heterogeneous Support in the Intel® Distribution for LINPACK
Benchmark.
If you want to run for N=10000 on a 1x3 grid, execute this command, provided that the other parameters in

HPL.dat and the script are correct:

runme_intel64_dynamic.bat -n 10000 -p 1 -q 3
By using the m parameter you can scale by the memory size instead of the problem size. The m parameter
only refers to the size of the matrix storage. Therefore, to use matrices that fit in 50000 Mbytes with
NB=256 on 16 nodes, adjust the script to set the total number of MPI processes to 16 and execute thiscommand:runme_intel64_dynamic.bat -m 50000 -b 256 -p 4 -q 4

Running the Intel® Distribution for LINPACK* Benchmark
To run the Intel® Distribution for LINPACK Benchmark on multiple nodes or on one node with multiple MPI
processes, you need to use MPI and either modify HPL.dat or use Ease-of-use Command-line Parameters.
The following example describes how to run the dynamically-linked prebuilt Intel® Distribution for LINPACK
Benchmark binary using the script provided. To run other binaries, adjust the steps accordingly.
1.

Load the necessary environment variables for the Intel MPI Library and Intel® compiler:<compiler directory>\env\vars.bat
2.

<mpi directory>\env\vars.bat
In HPL.dat, set the problem size N to 10000. Because this setting is for a test run, the problem size

should be small.

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3.

12

For better performance, enable non-uniform memory access (NUMA) on your system and configure to
run an MPI process for each NUMA socket as explained below.
NOTE
High-bandwidth Multi-Channel Dynamic Random Access Memory (MCDRAM) on the second-generation
Intel® Xeon® Phi processors may appear to be a NUMA node. However, because there are no CPUs on
this node, do not run an MPI process for it.
••

Refer to your BIOS settings to enable NUMA on your system.
Set the following variables at the top of the runme_intel64_dynamic.bat script according to yourcluster configuration:

MPI_PROC_NUM

4.
5.

The total number of MPI processes.

The number of MPI processes per each cluster node.
MPI_PER_NODE• In the HPL.dat file, set the parameters Ps and Qs so that Ps * Qs equals the number of MPI
processes. For example, for 2 processes, set Ps to 1 and Qs to 2. Alternatively, leave the HPL.dat
file as is and launch with -p and -q command-line parameters.
Execute runme_intel64_dynamic.bat script:

runme_intel64_dynamic.bat

Rerun the test increasing the size of the problem until the matrix size uses about 80% of the available
memory. To do this, either modify Ns in line 6 of HPL.dat or use the -n command-line parameter:•••

For 16 GB: 40000 Ns
For 32 GB: 56000 Ns
For 64 GB: 83000 Ns

See Also
Notational Conventions
Building the Intel® Distribution for LINPACK Benchmark for a Customized MPI Implementation
Building the Netlib HPL from Source CodeHeterogeneous Support in the Intel® Distribution for LINPACK* Benchmark
Intel® Distribution for LINPACK Benchmark achieves heterogeneous support by distributing the matrix data
unequally between the nodes. The heterogeneous factor command-line parameter f controls the amount ofwork to be assigned to the more powerful nodes, while the command-line parameter c controls the numberof process columns for the faster nodes:

xhpl.exe –n <problem size> –b <block size> –p <grid row dimn> –q <grid column dimn> –f<heterogeneous factor> –c <number of faster processor columns>
If the heterogeneous factor is 2.5, roughly 2.5 times the work will be put on the more powerful nodes. The
more work you put on the more powerful nodes, the more memory you might be wasting on the other nodes
if all nodes have equal amount of memory. If your cluster includes many different types of nodes, you may
need multiple heterogeneous factors.
Let P be the number of rows and Q the number of columns in your processor grid (PxQ). The work must be
homogeneous within each processor column because vertical operations, such as pivoting or panel
factorization, are synchronizing operations. When there are two different types of nodes, use MPI to process
all the faster nodes first and make sure the "PMAP process mapping" (line 9) of HPL.dat is set to 1 forColumn-major mapping. Because all the nodes must be the same within a process column, the number offaster nodes must always be a multiple of P, and you can specify the faster nodes by setting the number ofprocess columns C for the faster nodes with the c command-line parameter. The -f 1.0 -c 0 setting
corresponds to the default homogeneous behavior.
To understand how to choose the problem size N for a heterogeneous run, first consider a homogeneous
system, where you might choose N as follows:
N ~= sqrt(Memory Utilization * P * Q * Memory Size in Bytes / 8)

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Memory Utilization is usually around 0.8 for homogeneous Intel® Xeon® processor systems. On aheterogeneous system, you may apply a different formula for N for each set of nodes that are the same andthen choose the minimum N over all sets. Suppose you have a cluster with only one heterogeneous factor F
and the number of processor columns (out of the total Q) in the group with that heterogeneous factor equal
to C. That group contains P*C nodes. First compute the sum of the parts: S =F*P*C + P*(Q-C). Note that on
a homogeneous system S=P*Q,F=1, and C=Q. Take N as
N ~= sqrt(Memory Utilization * P * Q * ((F*P*C)/S) * Memory Size in Bytes / 8)
or simply scale down the value of N for the homogeneous system by sqrt(F*P*C/S).

ExampleSuppose the cluster has 100 nodes each having 64 GB of memory, and 20 of the nodes are 2.7 times as
powerful as the other 80. Run one MPI process per node for a total of 100 MPI processes. Assume a squareprocessor grid P=Q=10, which conveniently divides up the faster nodes evenly. Normally, the HPL
documentation recommends choosing a matrix size that consumes 80 percent of available memory. If N is
the size of the matrix, the matrix consumes 8N^2/(P*Q) bytes. So a homogeneous run might look like:

xhpl.exe –n 820000 –b 256 –p 10 –q 10
If you redistribute the matrix and run the heterogeneous Intel® Distribution for LINPACK Benchmark, you cantake advantage of the faster nodes. But because some of the nodes will contain 2.7 times as much data as
the other nodes, you must shrink the problem size (unless the faster nodes also happen to have 2.7 times asmuch memory). Instead of 0.8*64GB*100 total memory size, we have only 0.8*64GB*20 + 0.8*64GB/
2.7*80 total memory size, which is less than half the original space. So the problem size in this case would
be 526000. Because P=10 and there are 20 faster nodes, two processor columns are faster. If you arrange
MPI to send these nodes first to the application, the command line looks like:

xhpl.exe –n 526000 –b 1024 –p 10 –q 10 –f 2.7 –c 2
The m parameter may be misleading for heterogeneous calculations because it calculates the problem size
assuming all the nodes have the same amount of data.
Warning
The number of faster nodes must be C*P. If the number of faster nodes is not divisible by P, you
might not be able to take advantage of the extra performance potential by giving the faster nodes
extra work.
While it suffices to simply provide f and c command-line parameters if you need only one heterogeneous
factor, you must add lines to the HPL.dat input to support multiple heterogeneous factors. For the above
example (two processor columns have nodes that are 2.7 times faster), instead of passing f and ccommand-line parameters you can modify the HPL.dat input file by adding these two lines to the end:1
0 1 2.7

number of heterogeneous factors[start_column, stop_column, heterogeneous factor for that range]

NOTE
Numbering of processor columns starts at 0. The start and stopping numbers must be between 0 and
Q-1 (inclusive).
If instead there are three different types of nodes in a cluster and you need at least two heterogeneous
factors, change the number in the first row above from 1 to 2 and follow that line with two lines specifying
the start column, stopping column, and heterogeneous factor.
When choosing parameters for heterogeneous support in HPL.dat, primarily focus on the most powerfulnodes. The larger the heterogeneous factor, the more balanced the cluster may be from a performance
viewpoint, but the more imbalanced from a memory viewpoint. At some point, further performance balancing

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12

might affect the memory too much. If this is the case, try to reduce any changes done for the faster nodes(such as in block sizes). Experiment with values in HPL.dat carefully because wrong values may greatly
hinder performance.
When tuning on a heterogeneous cluster, do not immediately attempt a heterogeneous run, but do thefollowing:
1.
2.
3.
4.
5.

Break the cluster down into multiple homogeneous clusters.
Make heterogeneous adjustments for performance balancing. For instance, if you have two different
sets of nodes where one is three times as powerful as the other, it must do three times the work.
Figure out the approximate size of the problem (per node) that you can run on each piece.
Do some homogeneous runs with those problem sizes per node and the final block size needed for theheterogeneous run and find the best parameters.
Use these parameters for an initial heterogeneous run.

Environment Variables
The table below lists Intel® oneAPI Math Kernel Library environment variables to control runs of the Intel
Distribution for LINPACK Benchmark.
Environment Variable

Description

Value

HPL_LARGEPAGE

Defines the memory mapping to be
used for the Intel® Xeon® processor.

0 or 1:••

HPL_LOG

Controls the level of detail for the
HPL output.

An integer ranging from 0 to
2:
•
•

•

HPL_HOST_CORE, HPL_HOST_NODE

Specifies cores or Non-Uniform
Memory Access (NUMA) nodes tobe used.

HPL_HOST_NODE requires NUMA

mode to be enabled. You can check
whether it is enabled by thenumactl –-hardware command.

0 - normal memory
mapping, default.
1 - memory mapping withlarge pages (2 MB per
page mapping). It may
increase performance.

0 - no log is displayed.
1 - only one root node
displays a log, exactly thesame as the ASYOUGO
option provides.
2 - the most detailed log isdisplayed. All P root nodes
in the processor columnthat owns the current
column block display a log.

A list of integers ranging from0 to the largest number of acore or NUMA node in thecluster and separated as
explained in example 3.

The default behavior is autodetection of the core or NUMA
node.

HPL_SWAPWIDTH

Specifies width for each swap
operation.

16 or 24. The default is 24.

You can set Intel Distribution for LINPACK Benchmark environment variables using the PMI_RANK and
PMI_SIZE environment variables of the Intel MPI library, and you can create a shell script to automate theprocess.

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Examples of Environment Settings#

Settings

Behavior of the Intel Distribution for LINPACK
Benchmark1

Nothing specified

All Intel® Xeon® processors in the cluster are used.

2

HPL_MIC_DEVICE=0,2

Intel® Xeon® processor cores 1,2,3,8,9, and 10 are used.

HPL_HOST_CORE=1-3,8-10
3

HPL_HOST_NODE=1

Only Intel® Xeon® processor cores on NUMA node 1 are used.

Improving Performance of Your Cluster
To improve cluster performance, follow these steps, provided all required software is installed on each node:
1.
2.

3.
4.
5.
6.
7.

Reboot all nodes.
Ensure all nodes are in identical conditions and no zombie processes are left running from prior HPL
runs. To do this, run single-node Stream and Intel® Distribution for LINPACK Benchmark on every node.
Ensure results are within 10% of each other (problem size must be large enough depending on memory
size and CPU speed). Investigate nodes with low performance for hardware/software problems.
Check that your cluster interconnects are working. Run a test over the complete cluster using an MPI
test for bandwidth and latency, such as one found in the Intel® MPI Benchmarks package.
Run an Intel® Distribution for LINPACK Benchmark on pairs of two or four nodes and ensure results arewithin 10% of each other. The problem size must be large enough depending on the memory size andCPU speed.
Run a small problem size over the complete cluster to ensure correctness.
Increase the problem size and run the real test load.
In case of problems go back to step 2.

Before making a heterogeneous run, always run its homogeneous equivalent first.

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AIntel® oneAPI Math Kernel Library
Language Interfaces Support
See Also
Mixed-language Programming with Intel® oneAPI Math Kernel Library

Language Interfaces Support, by Function Domain
The following table shows language interfaces that Intel® oneAPI Math Kernel Library (oneMKL) provides foreach function domain. However, Intel® oneAPI Math Kernel Library routines can be called from otherlanguages using mixed-language programming. SeeMixed-language Programming with the Intel Math Kernel
Library for an example of how to call Fortran routines from C/C++.
Function Domain

Fortran int
erfaceC/C++interface

Basic Linear Algebra Subprograms (BLAS)

Yes

through
CBLAS

BLAS-like extension transposition routines

Yes

Yes

Sparse BLAS Level 1

Yesthrough
CBLAS

Sparse BLAS Level 2 and 3

Yes

Yes

LAPACK routines for solving systems of linear equations

Yes

Yes

LAPACK routines for solving least-squares problems, eigenvalue and singularvalue problems, and Sylvester's equations

Yes

Yes

Auxiliary and utility LAPACK routines
Parallel Basic Linear Algebra Subprograms (PBLAS)
ScaLAPACK

Yes

Yes

Yes
Yes

†

Direct Sparse Solvers/
Intel® oneAPI Math Kernel Library PARDISO, a direct sparse solver based on
Parallel Direct Sparse Solver (PARDISO*)

Yes

Yes

Parallel Direct Sparse Solvers for Clusters

Yes

Yes

Other Direct and Iterative Sparse Solver routines

Yes

Yes

Vector Mathematics (VM)

Yes

Yes

Vector Statistics (VS)

Yes

Yes

Fast Fourier Transforms (FFT)

Yes

Yes

Cluster FFT

Yes

Yes

Trigonometric Transforms

Yes

Yes

Fast Poisson, Laplace, and Helmholtz Solver (Poisson Library)

Yes

Yes

Optimization (Trust-Region) Solver

Yes

Yes

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Function Domain

Fortran int
erfaceC/C++interface

Data Fitting

Yes

Yes

Extended Eigensolver

Yes

Yes

Support functions (including memory allocation)

Yes

Yes

† Supported using a mixed language programming call. See Include Files for the respective header file.

Include Files
The table below lists Intel® oneAPI Math Kernel Library include files.
Function Domain/
PurposeFortran Include Files

C/C++ Include Files

All function domains

mkl.fi

mkl.h
mkl_blacs.h‡‡

BLACS
BLAS

blas.f90
mkl_blas.fi†

mkl_blas.h‡

BLAS-like Extension Transposition
Routines

mkl_trans.fi†

mkl_trans.h‡
mkl_cblas.h‡

CBLAS Interface to BLAS
Sparse BLAS

mkl_spblas.fi†

mkl_spblas.h‡

LAPACK

lapack.f90
mkl_lapack.fi†

mkl_lapack.h‡

C Interface to LAPACK

mkl_lapacke.h‡

PBLAS

mkl_pblas.h‡‡

ScaLAPACK

mkl_scalapack.h‡‡Intel® oneAPI Math Kernel Library
PARDISO

mkl_pardiso.f90
mkl_pardiso.fi†

mkl_pardiso.h‡

Parallel Direct Sparse Solvers for
Clusters

mkl_cluster_sparse_solver.f90

mkl_cluster_sparse_solver.h‡Direct Sparse Solver (DSS)

mkl_dss.f90
mkl_dss.fi†

mkl_dss.h‡

mkl_rci.f90
mkl_rci.fi†

mkl_rci.h‡

Optimization Solver

mkl_rci.f90
mkl_rci.fi†

mkl_rci.h‡

Vector Mathematics

mkl_vml.90
mkl_vml.fi†

mkl_vml.h‡

Vector Statistics

mkl_vsl.f90
mkl_vsl.fi†

mkl_vsl.h‡

RCI Iterative Solvers
ILU Factorization

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Function Domain/
PurposeFortran Include Files

C/C++ Include Files

Fast Fourier Transforms

mkl_dfti.f90

mkl_dfti.h‡

Cluster Fast Fourier Transforms

mkl_cdft.f90

mkl_cdft.h‡‡Trigonometric Transforms

mkl_trig_transforms.f90

mkl_trig_transform.h‡

Poisson Solvers

mkl_poisson.f90

mkl_poisson.h‡

Data Fitting

mkl_df.f90

mkl_df.h‡Extended Eigensolver

mkl_solvers_ee.fi†

mkl_solvers_ee.h‡Support functions

mkl_service.f90
mkl_service.fi†

mkl_service.h‡Partial Differential Equations Support

Declarations for replacing memory
allocation functions. See Redefining
Memory Functions for details.
Auxiliary macros to determine theversion of Intel® oneAPI Math Kernel
Library at compile time.

i_malloc.h

mkl_version

mkl_version‡† You can use the mkl.fi include file in your code instead.
‡ You can include the mkl.h header file in your code instead.
‡‡ Also include the mkl.h header file in your code.

See Also
Language Interfaces Support, by Function Domain

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Support for Third-Party Interfaces

B

FFTW Interface Support
Intel® oneAPI Math Kernel Library (oneMKL) offers two collections of wrappers for the FFTW interface(www.fftw.org). The wrappers are the superstructure of FFTW to be used for calling the Intel® oneAPI Math
Kernel Library Fourier transform functions. These collections correspond to the FFTW versions 2.x and 3.x
and the Intel® oneAPI Math Kernel Library versions 7.0 and later.
These wrappers enable using Intel® oneAPI Math Kernel Library Fourier transforms to improve theperformance of programs that use FFTW without changing the program source code. See the "FFTW Interface
to Intel® oneAPI Math Kernel Library" appendix in the Intel® oneAPI Math Kernel Library Developer Reference
for details on the use of the wrappers.
Important
For ease of use, the FFTW3 interface is also integrated in Intel® oneAPI Math Kernel Library.

CautionThe FFTW2 and FFTW3 interfaces are not compatible with each other. Avoid linking to both of them. If
you must do so, first modify the wrapper source code for FFTW2:
1.
2.
3.

120

Change every instance of fftw_destroy_plan in the fftw2xc interface tofftw2_destroy_plan.

Change all the corresponding file names accordingly.
Rebuild the pertinent libraries.

Directory Structure in DetailC

C

Directory Structure in Detail

Tables in this section show contents of the Intel® oneAPI Math Kernel Library (oneMKL) architecture-specificdirectories.
Product and Performance Information
Performance varies by use, configuration and other factors. Learn more at www.Intel.com/
PerformanceIndex.
Notice revision #20201201

See Also
High-level Directory Structure
Using Language-Specific Interfaces with Intel® oneAPI Math Kernel LibraryIntel® oneAPI Math Kernel Library Benchmarks

Detailed Structure of the IA-32 Architecture Directories
Static Libraries in the lib\ia32 Directory
Some of the libraries in this directory are optional. However, some optional libraries are installed by default,
while the rest are not. To get those libraries that are not installed by default, explicitly select the specified
optional component during installation.
File

Contents

Optional Component
Name

Installed by
Default

Interface Layer

mkl_intel_c.lib

cdecl interface library

mkl_blas95.lib

Fortran 95 interface
library for BLAS.
Supports the Intel®
Fortran compiler

Fortran 95
interfaces for BLAS
and LAPACKYes

mkl_lapack95.lib

Fortran 95 interface
library for LAPACK.
Supports the Intel®
Fortran compiler

Fortran 95
interfaces for BLAS
and LAPACK

Yes

Threading Layer

mkl_intel_thread.lib

OpenMP threading
library for the Intel
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File

Contents

mkl_tbb_thread.lib

Intel® Threading
Building Blocks (Intel®
TBB) threading libraryfor the Intel compilers

mkl_sequential.lib

Sequential library

Optional Component
Name

Installed by
Default

Intel TBB
threading support

Yes

Computational Layer

mkl_core.lib

Kernel library for IA-32
architecture

Dynamic Libraries in the lib\ia32 Directory
Some of the libraries in this directory are optional. However, some optional libraries are installed by default,
while the rest are not. To get those libraries that are not installed by default, explicitly select the specified
optional component during installation.
File

mkl_rt.lib

Contents

Optional Component
Name

Installed by
Default

Intel TBB
threading support

Yes

Single Dynamic Library
to be used for linking

Interface Layer

mkl_intel_c_dll.lib

cdecl interface libraryfor dynamic linking

Threading Layer

mkl_intel_thread_dll.lib

OpenMP threading
library for dynamic
linking with the Intel
compilers

mkl_tbb_thread_dll.lib

Intel TBB threading
library for the Intel
compilers

mkl_sequential_dll.lib

Sequential library for
dynamic linking

Computational Layer

mkl_core_dll.lib

Core library for dynamic
linking

Contents of the redist\ia32 Directory
Some of the libraries in this directory are optional. However, some optional libraries are installed by default,
while the rest are not. To get those libraries that are not installed by default, explicitly select the specified
optional component during installation.

122

Directory Structure in DetailFilemkl_rt.dll

Contents

C

Optional Component
Name

Installed by
Default

Intel TBB
threading support

Yes

Single Dynamic LibraryThreading Layer

mkl_intel_thread.dll

Dynamic OpenMP
threading library for theIntel compilers

mkl_tbb_thread.dll

Dynamic Intel TBB
threading library for theIntel compilers

mkl_sequential.dll

Dynamic sequential
library

Computational Layer

mkl_core.dll

Core library containing
processor-independentcode and a dispatcher
for dynamic loading ofprocessor-specific code

mkl_p4.dll

Pentium® 4 processor
kernelmkl_p4m.dll

Kernel library for Intel®
Supplemental Streaming
SIMD Extensions 3(Intel® SSSE3) enabled
processors

mkl_p4m3.dll

Kernel library for Intel®
Streaming SIMD
Extensions 4.2 (Intel®
SSE4.2) enabled
processors

mkl_avx.dll

Kernel library for Intel®
Advanced Vector
Extensions (Intel® AVX)
enabled processors

mkl_avx2.dll

Kernel library for Intel®
Advanced Vector
Extensions 2 (Intel®
AVX2) enabled
processors

mkl_avx512.dll

Kernel library for Intel®
Advanced Vector
Extensions 512 (Intel®
AVX-512) enabled
processors

123

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File

Contents

Optional ComponentName

mkl_vml_p4.dll

Vector Mathematics(VM)/Vector Statistics(VS)/Data Fitting (DF)part of Pentium® 4
processor kernel

mkl_vml_p4m.dll

VM/VS/DF for Intel®
SSSE3 enabled
processors

mkl_vml_p4m2.dll

VM/VS/DF for 45nm Hik Intel® Core™2 and Intel
Xeon® processor families

mkl_vml_p4m3.dll

VM/VS/DF for Intel®
SSE4.2 enabled
processors

mkl_vml_avx.dll

VM/VS/DF optimized forIntel® AVX enabled
processors

mkl_vml_avx2.dll

VM/VS/DF optimized forIntel® AVX2 enabled
processors

mkl_vml_avx512.dll

VM/VS/DF optimized forIntel® AVX-512 enabled
processors

mkl_vml_ia.dll

VM/VS/DF default kernel
for newer Intel®
architecture processors

libmkl_vml_cmpt.dll

VM/VS/DF library forconditional numericalreproducibility

libimalloc.dll

Dynamic library tosupport renaming of
memory functions

Message Catalogs1033\mkl_msg.dll

Catalog of Intel® oneAPI
Math Kernel Library(oneMKL) messages inEnglish1041\mkl_msg.dll

Catalog of Intel® oneAPI
Math Kernel Librarymessages in Japanese.
Available only if Intel®
oneAPI Math Kernel
Library provides
Japanese localization.

124

Installed by
DefaultDirectory Structure in Detail

File

Contents

C

Optional Component
Name

Installed by
Default

Please see the Release
Notes for thisinformation.

Detailed Structure of the Intel® 64 Architecture Directories
Static Libraries in the lib\intel64 Directory
Some of the libraries in this directory are optional. However, some optional libraries are installed by default,
while the rest are not. To get those libraries that are not installed by default, explicitly select the specified
optional component during installation.
File

Contents

mkl_sycl.lib

DPC++ library for theIntel DPC++ compilers

mkl_sycld.lib

DPC++ library for theIntel DPC++ compiler
with debug runtime (/MDd)

Optional Component
Name

Installed by
Default

Interface Layer

mkl_intel_lp64.lib

LP64 interface library for
the Intel compilers

mkl_intel_ilp64.lib

ILP64 interface libraryfor the Intel compilers

mkl_blas95_lp64.lib

Fortran 95 interface
library for BLAS.
Supports the Intel®
Fortran compiler andLP64 interface

Fortran 95
interfaces for BLAS
and LAPACK

Yes

mkl_blas95_ilp64.lib

Fortran 95 interface
library for BLAS.
Supports the Intel®
Fortran compiler and
ILP64 interface

Fortran 95
interfaces for BLAS
and LAPACKYes

mkl_lapack95_lp64.lib

Fortran 95 interface
library for LAPACK.
Supports the Intel®
Fortran compiler andLP64 interface

Fortran 95
interfaces for BLAS
and LAPACK

Yes

125

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Intel® oneAPI Math Kernel Libraryfor Windows* Developer GuideFilemkl_lapack95_ilp64.lib

Contents

Fortran 95 interface
library for LAPACK.
Supports the Intel®
Fortran compiler and
ILP64 interface

Optional Component
Name

Installed by
Default

Fortran 95
interfaces for BLAS
and LAPACK

Yes

Threading Layer

mkl_intel_thread.lib

OpenMP threading
library for the Intel
compilers

mkl_tbb_thread.lib

Intel® Threading
Building Blocks (Intel®
TBB) threading libraryfor the Intel compilers

Intel TBB
threading supportYes

mkl_tbb_threadd.lib

Intel® Threading
Building Blocks (Intel®
TBB) threading libraryfor the Intel compilers
compatible withIntel TBB
threading supportYes

mkl_sycld.lib
mkl_pgi_thread.lib

OpenMP threading
library for the PGI*
compiler

mkl_sequential.lib

Sequential library

PGI* Compiler
supportComputational Layer

mkl_core.lib

Kernel library for theIntel® 64 architecture

Cluster Libraries

mkl_scalapack_lp64.lib

ScaLAPACK routine
library supporting theLP64 interface

Cluster support

mkl_scalapack_ilp64.lib

ScaLAPACK routine
library supporting the
ILP64 interface

Cluster support

mkl_cdft_core.lib

Cluster version of FFTs

Cluster support

mkl_blacs_intelmpi_lp64.lib

LP64 version of BLACS
routines supportingIntel® MPI Library

Cluster support

mkl_blacs_intelmpi_ilp64.lib

ILP64 version of BLACS
routines supporting IntelMPI Library

Cluster support

mkl_blacs_msmpi_lp64.lib

LP64 version of BLACS
routines supporting
Microsoft* MPICluster support

126

Directory Structure in Detail

File

Contents

Optional ComponentName

mkl_blacs_msmpi_ilp64.lib

ILP64 version of BLACS
routines supporting
Microsoft* MPICInstalled by
Default

Cluster support

Dynamic Libraries in the lib\intel64 Directory
Some of the libraries in this directory are optional. However, some optional libraries are installed by default,
while the rest are not. To get those libraries that are not installed by default, explicitly select the specified
optional component during installation.
File

Contents

mkl_sycl_dll.lib

DPC++ library for theIntel DPC++ compilers

mkl_sycld_dll.lib

DPC++ library for theIntel DPC++ compiler
with debug runtime (/MDd)

mkl_rt.lib

Single Dynamic Library
to be used for linking

Optional Component
Name

Installed by
Default

Interface Layer

mkl_intel_lp64_dll.lib

LP64 interface library for
dynamic linking with theIntel compilers

mkl_intel_ilp64_dll.lib

ILP64 interface libraryfor dynamic linking with
the Intel compilers

Threading Layer

mkl_intel_thread_dll.lib

OpenMP threading
library for dynamic
linking with the Intel
compilers

mkl_tbb_thread_dll.lib

Intel TBB threading
library for the Intel
compilersIntel TBB
threading supportYes

mkl_tbb_threadd_dll.lib

Intel® Threading
Building Blocks (Intel®
TBB) threading libraryfor the Intel compilers
compatible withIntel® TBB
threading supportYes

mkl_sycld_dll.lib

127

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File

Contents

Optional ComponentNamemkl_pgi_thread_dll.lib

OpenMP threading
library for dynamic
linking with the PGI*
compiler

mkl_sequential_dll.lib

Sequential library for
dynamic linking

Installed by
Default

PGI* Compiler
supportComputational Layer

mkl_core_dll.lib

Core library for dynamic
linking

Cluster Libraries

mkl_scalapack_lp64_dll.lib

ScaLAPACK routine
library for dynamic
linking supporting theLP64 interface

Cluster support

mkl_scalapack_ilp64_dll.lib

ScaLAPACK routine
library for dynamic
linking supporting the
ILP64 interface

Cluster support

mkl_cdft_core_dll.lib

Cluster FFT library for
dynamic linking

Cluster support

mkl_blacs_lp64_dll.lib

LP64 version of BLACS
interface library for
dynamic linking

Cluster support

mkl_blacs_ilp64_dll.lib

ILP64 version of BLACS
interface library for
dynamic linking

Cluster support

Contents of the redist\intel64 Directory
Some of the libraries in this directory are optional. However, some optional libraries are installed by default,
while the rest are not. To get those libraries that are not installed by default, explicitly select the specified
optional component during installation.
File

Contents

Optional ComponentName

mkl_sycl.<version>.dll

DPC++ library for theIntel DPC++ compilers

mkl_sycld.<version>.dll

DPC++ library for theIntel DPC++ compiler
with debug runtime (/MDd)

mkl_rt.<version>.dll

Single Dynamic Library

128

Installed by
DefaultDirectory Structure in Detail

File

Contents

C

Optional Component
Name

Installed by
Default

Yes

Threading layer

mkl_tbb_threadd.<version>.dll

Dynamic Intel®
Threading Building
Blocks (Intel® TBB)
threading library for theIntel compilers
compatible with

mkl_sycld.dll
mkl_intel_thread.<version>.dll

Dynamic OpenMP
threading library for theIntel compilers

mkl_tbb_thread.<version>.dll

Dynamic Intel TBB
threading library for theIntel compilers

Intel TBB
threading support

mkl_pgi_thread.<version>.dll

Dynamic OpenMP
threading library for the
PGI* compiler

PGI* compiler
supportmkl_sequential.<version>.dll

Dynamic sequential
library

Computational layer

mkl_core.<version>.dll

Core library containing
processor-independentcode and a dispatcher
for dynamic loading ofprocessor-specific code

mkl_def.<version>.dll

Default kernel for theIntel® 64 architecture

mkl_mc.<version>.dll

Kernel library for Intel®
Supplemental Streaming
SIMD Extensions 3(Intel® SSSE3) enabled
processors

mkl_mc3.<version>.dll

Kernel library for Intel®
Streaming SIMD
Extensions 4.2 (Intel®
SSE4.2) enabled
processors

mkl_avx.<version>.dll

Kernel library optimized
for Intel® Advanced
Vector Extensions(Intel® AVX) enabled
processors

129

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Intel® oneAPI Math Kernel Libraryfor Windows* Developer Guide

File

Contents

Optional ComponentNamemkl_avx2.<version>.dll

Kernel library optimized
for Intel® Advanced
Vector Extensions 2(Intel® AVX2) enabled
processors

mkl_avx512.<version>.dll

Kernel library optimized
for Intel® Advanced
Vector Extensions 512(Intel® AVX-512)
enabled processors

mkl_vml_def.<version>.dll

Vector Mathematics(VM)/Vector Statistics(VS)/Data Fitting (DF)part of default kernel

mkl_vml_mc.<version>.dll

VM/VS/DF for Intel®
SSSE3 enabled
processors

mkl_vml_mc2.<version>.dll

VM/VS/DF for 45nm Hik Intel® Core™2 and Intel
Xeon® processor families

mkl_vml_mc3.<version>.dll

VM/VS/DF for Intel®
SSE4.2 enabled
processors

mkl_vml_avx.<version>.dll

VM/VS/DF optimized forIntel® AVX enabled
processors

mkl_vml_avx2.<version>.dll

VM/VS/DF optimized forIntel® AVX2 enabled
processors

mkl_vml_avx512.<version>.dll

VM/VS/DF optimized forIntel® AVX-512 enabled
processors

mkl_vml_cmpt.<version>.dll

VM/VS/DF library forconditional numericalreproducibility

libimalloc.dll

Dynamic library tosupport renaming of
memory functions

Cluster Libraries

mkl_scalapack_lp64.<version>.dll

ScaLAPACK routine
library supporting theLP64 interface

Cluster support

mkl_scalapack_ilp64.<version>.dll

ScaLAPACK routine
library supporting the
ILP64 interface

Cluster support

130

Installed by
DefaultDirectory Structure in Detail

File

Contents

C

Optional ComponentName

mkl_cdft_core.<version>.dll

Cluster FFT dynamic
library

Cluster support

mkl_blacs_lp64.<version>.dll

LP64 version of BLACS
routines

Cluster support

mkl_blacs_ilp64.<version>.dll

ILP64 version of BLACS
routines

Cluster support

mkl_blacs_intelmpi_lp64.<version>.dll

LP64 version of BLACS
routines for Intel® MPI
Library

Cluster support

mkl_blacs_intelmpi_ilp64.<version>.dllILP64 version of BLACS
routines for Intel MPI
Library

Cluster support

mkl_blacs_msmpi_lp64.<version>.dllLP64 version of BLACS
routines for Microsoft*
MPI

Cluster support

mkl_blacs_msmpi_ilp64.<version>.
dll

ILP64 version of BLACS
routines for Microsoft*
MPI

Cluster support

Installed by
Default

Message Catalogs1033\mkl_msg.dll

Catalog of Intel® oneAPI
Math Kernel Library(oneMKL) messages inEnglish131

Intel® oneAPI Math Kernel Libraryfor Windows* Developer Guide

Index
A

custom DLL (continued)
specifying function names 38

affinity mask 57
aligning data, example 80
architecture support 18

B
BLAS
calling routines from C 67
Fortran 95 interface to 65
OpenMP* threaded routines 43
building a custom DLL
in Visual Studio* IDE 38

C
C interface to LAPACK, use of 67
C, calling LAPACK, BLAS, CBLAS from 67
C/C++, Intel(R) MKL complex types 68
calling
BLAS functions from C 69
CBLAS interface from C 69
complex BLAS Level 1 function from C 69
complex BLAS Level 1 function from C++ 69
Fortran-style routines from C 67
calling convention, cdecl 14
CBLAS interface, use of 67
Cluster FFT
environment variable for 99, 100
linking with 91
managing performance of 99, 100
cluster software, Intel(R) MKL 90
cluster software, linking withcommands 91
linking examples 95
Cluster Sparse Solver, linking with 91
code examples, use of 15
codingdata alignment 80
techniques to improve performance 61
compilation, Intel(R) MKL version-dependent 81
compiler run-time libraries, linking with 34
compiler support 14
compiler-dependent function 66
complex types in C and C++, Intel(R) MKL 68
computation results, consistency 72
computational libraries, linking with 33
conditional compilation 81
configuringIntel(R) Visual Fortran 104
Microsoft Visual* C/C++ 103
consistent results 72
context-sensitive Help, for Intel(R) MKL, in Visual Studio*
IDE 104
conventions, notational 10
custom DLL
building 34
composing list of functions 37

132

D
data alignment, example 80
denormal number, performance 62
direct call, to Intel(R) Math Kernel Library computational
kernels 58
directory structurehigh-level 18
in-detaildispatch Intel(R) architectures, configure with anenvironment variable 101
dispatch, new Intel(R) architectures, enable with anenvironment variable 101

E
Enter index keyword 21
environment variables
for threading control 50
setting for specific architecture and programming
interface 14
to control dispatching for Intel(R) architectures 101
to control threading algorithm for ?gemm 54
to enable dispatching of new architectures 101
to manage behavior of function domains 98
to manage behavior of Intel(R) Math Kernel Librarywith 98
to manage performance of cluster FFT 99
examples, linking
for cluster software 95
general 26

F
FFT interface
OpenMP* threaded problems 43
FFTW interface support 120
Fortran 95 interface libraries 31
function call information, enable printing 83

Hheader files, Intel(R) MKL 118
heterogeneityof Intel(R) Distribution for LINPACK* Benchmark 109
heterogeneous cluster
support by Intel(R) Distribution for LINPACK*
Benchmark for Clusters 113
heterogeneous cores 58
HT technology, configuration tip 56

I
ILP64 programming, support for 29
improve performance, for matrices of small sizes 58

Indexinclude files, Intel(R) MKL 118
information, for function call , enable printing 83
installation, checking 12, 13Intel(R) Distribution for LINPACK* Benchmark
heterogeneity of 109
Intel(R) Distribution for LINPACK* Benchmark for Clusters
heterogeneous support 113
Intel(R) Hyper-Threading Technology, configuration tip 56
Intel(R) Visual* Fortran project, linking with Intel(R)MKL 22
Intel® Threading Building Blocks, functions threaded with 45
IntelliSense*, with Intel(R) MKL, in Visual Studio* IDE 104
interface
Fortran 95, libraries 31
LP64 and ILP64, use of 29
interface libraries and modules, Intel(R) MKL 64
interface libraries, linking with 29

K

Microsoft Visual* C/C++ project, linking with Intel(R)MKL 22
mixed-language programming 66
module, Fortran 95 65
MPI
custom, usage 94
Intel(R) Math Kernel Library interaction with 93
support 90
multi-core performance 57

Nnotational conventions 10
number of threads
changing at run time 48
changing with OpenMP* environment variable 47
Intel(R) MKL choice, particular cases 51
setting for cluster 92
techniques to set 47
numerically reproducible results 72

kernel, in Intel(R) Math Kernel Library, direct call to 58

Llanguage interfaces support 117
language-specific interfaces
interface libraries and modules 64
LAPACK
C interface to, use of 67
calling routines from C 67
Fortran 95 interface to 65
OpenMP* threaded routines 43
performance of packed routines 61
layers, Intel(R) MKL structure 19
libraries to link withcomputational 33
interface 29
run-time 34
system libraries 34
threading 31
link tool, command line 24
linking
Intel(R) Visual* Fortran project with Intel(R) MKL 22
Microsoft Visual* C/C++ project with Intel(R) MKL 22
linking examples
cluster software 95
general 26
linking withcompiler run-time libraries 34
computational libraries 33
interface libraries 29
system libraries 34
threading libraries 31
linking, quick start 21, 39
linking, Web-based advisor 24
LINPACK benchmark 107

Mmemory functions, redefining 62
memory management 62
memory renaming 62
message-passing interface
custom, usage 94
Intel(R) Math Kernel Library interaction with 93
support 90

O
OpenMP* threaded functions 43
OpenMP* threaded problems 43

Pparallel performance 46
parallelism, of Intel(R) MKL 43
performance
heterogeneous cores 58
multi-core 57
with denormals 62
with subnormals 62
performance improvement, for matrices of small sizes 58
performance, of Intel(R) MKL, improve on specificprocessors 62

Rresults, consistent, obtaining 72
results, numerically reproducible, obtaining 72

S
ScaLAPACK, linking with 91
SDL 23, 28
Single Dynamic Library 23, 28
structurehigh-level 18
in-detailmodel 19
support, technical 7
supported architectures 18
system libraries, linking with 34

Ttechnical support 7
thread safety, of Intel(R) MKL 43
threaded functions, with Intel® Threading Building Blocks 45
threading control, Intel(R) MKL-specific 50
threading libraries, linking with 31

133

Intel® oneAPI Math Kernel Libraryfor Windows* Developer GuideU
unstable output, getting rid of 72

V
Vector Mathematics
default mode, setting with environment variable 98environment variable to set default mode 98
verbose mode, of Intel(R) MKL 83
Visual Studio* IDEIntelliSense*, with Intel(R) MKL 104
using Intel(R) MKL context-sensitive Help in 104

134