Intel Xeon Phi processor high performance programming Note continued: Parallel Programming for Noncomputer Scientists Step by Step Improvements N Body Simulation Optimization Initial Implementation (Optimization Step 0) Thread Parallelism (Optimization Step 1) Scalar Performance Tuning (Optimization Step 2) Vectorization with SOA (Optimization Step 3) Memory Traffic (Optimization Step 4) Impact of MCDRAM on Performance Summary For More Information ch. 24 Machine Learning Convolutional Neural Networks OverFeat FAST Results For More Information ch. 25 Trinity Workloads Out of the Box Performance Optimizing MiniGhost OpenMP Performance Summary For More Information ch. 26 Quantum Chromodynamics LQCD The QPhiX Library and Code Generator Wilson Dslash Operator Configuring the QPhiX Code Generator The Experimental Setup Results Conclusion For More Information. Note continued: Intel Data Analytics Library Overview Together: MKL and DAAL Intel Integrated Performance Primitives Library Overview Intel Performance Libraries and Intel Compilers Native (Direct) Library Usage Offloading to Knights Landing While Using a Library Precision Choices and Variations Performance Tip for Faster Dynamic Libraries For More Information ch. 14 Profiling and Timing Introduction to Knight Landing Tuning Event Monitoring Registers Efficiency Metrics Potential Performance Issues Intel VTune Amplifier XE Product Performance Application Programming Interface MPI Analysis: ITAC HPCToolkit Tuning and Analysis Utilities Timing Summary For More Information ch. 15 MPI Internode Parallelism MPI on Knights Landing MPI Overview How to Run MPI Applications Analyzing MPI Application Runs Tuning of MPI Applications Heterogeneous Clusters Recent Trends in MPI Coding. Machine generated contents note: ch. 1 Introduction Introduction to Many Core Programming Trend: More Parallelism Why Intel® Xeon Phi[ ! Processors Are Needed Processors Versus Coprocessor Measuring Readiness for Highly Parallel Execution What About GPUs Enjoy the Lack of Porting Needed but Still Tune! Transformation for Performance Hyper Threading Versus Multithreading Programming Models Why We Could Skip To Section II Now For More Information ch. 2 Knights Landing Overview Overview Instruction Set Architecture Overview Motivation: Our Vision and Purpose Summary For More Information ch. 3 Programming MCDRAM and Cluster Modes Programming for Cluster Modes Programming for Memory Modes Query Memory Mode and MCDRAM Available SNC Performance Implications of Allocation and Threading How to Not Hard Code the NUMA Node Numbers Approaches to Determining What to Put in MCDRAM. Note continued: Know When to Holdem and When to Foldem For More Information ch. 8 Tasks and Threads OpenMP Fortran 2008 Intel TBB hStreams Summary For More Information ch. 9 Vectorization Why Vectorize How to Vectorize Three Approaches to Achieving Vectorization Six Step Vectorization Methodology Streaming Through Caches: Data Layout Alignment Prefetching and so on Compiler Tips Compiler Options Compiler Directives Use Array Sections to Encourage Vectorization Look at What the Compiler Created: Assembly Code Inspection Numerical Result Variations with Vectorization Summary For More Information ch. 10 Vectorization Advisor Getting Started with Intel Advisor for Knights Landing Enabling and Improving AVX 512 Code with the Survey Report Memory Access Pattern Report AVX 512 Gather Scatter Profiler Mask Utilization and FLOPS Profiler Advisor Roofline Report. Note continued: Hardware Based Power Analysis Techniques Software Based Knights Landing Power Analyzer ManyCore Platform Software Package Power Tools Running Average Power Limit Performance Profiling on Knights Landing Intel Remote Management Module Summary For More Information ch. 20 Optimizing Classical Molecular Dynamics in LAMMPS Molecular Dynamics LAMMPS Knights Landing Processors LAMMPS Optimizations Data Alignment Data Types and Layout Vectorization Neighbor List Long Range Electrostatics MPI and OpenMP Parallelization Performance Results System Build and Run Configurations Workloads Organic Photovoltaic Molecules Hydrocarbon Mixtures Rhodopsin Protein in Solvated Lipid Bilayer Coarse Grain Liquid Crystal Simulation Coarse Grain Water Simulation Summary Acknowledgment For More Information ch. 21 High Performance Seismic Simulations High Order Seismic Simulations. Note continued: Why Rebooting Is Required to Change Modes BIOS Summary For More Information ch. 4 Knights Landing Architecture Tile Architecture Cluster Modes Memory Interleaving Memory Modes Interactions of Cluster and Memory Modes Summary For More Information ch. 5 Intel Omni Path Fabric Overview Performance and Scalability Transport Layer APIs Quality of Service Virtual Fabrics Unicast Address Resolution Multicast Address Resolution Summary For More Information ch. 6 [æ]arch Optimization Advice Best Performance From 1 2 or 4 Threads Per Core Rarely 3 Memory Subsystem [æ]arch Nuances (Tile) Direct Mapped MCDRAM Cache Advice: Use AVX 512 Summary For More Information ch. 7 Programming Overview for Knights Landing To Refactor or Not to Refactor That Is the Question Evolutionary Optimization of Applications Revolutionary Optimization of Applications. Note continued: Putting it all Together Summary For More Information ch. 16 PGAS Programming Models To Share or not to Share Why Use PGAS on Knights Landing Programming with PGAS Performance Evaluation Beyond PGAS Summary For More Information ch. 17 Software Defined Visualization Motivation for Software Defined Visualization Software Defined Visualization Architecture OpenSWR: OpenGL Raster Graphics Software Rendering Embree: High Performance Ray Tracing Kernel Library OSPRay: Scalable Ray Tracing Framework Summary Image Attributions For More Information ch. 18 Offload to Knights Landing Offload Programming Model Using with Knights Landing Processors Versus Coprocessor Offload Model Considerations OpenMP Target Directives Concurrent Host and Target Execution Offload Over Fabric Summary For More Information ch. 19 Power Analysis Power Demand Gates Exascale Power 101. Note continued: Numerical Background Application Characteristics Intel Architecture as Compute Engine Highly Efficient Small Matrix Kernels Sparse Matrix Kernel Generation and Sparse Dense Kernel Selection Dense Matrix Kernel Generation: AVX2 Dense Matrix Kernel Generation: AVX 512 Kernel Performance Benchmarking Incorporating Knights Landings Different Memory Subsystems Performance Evaluation Mount Merapi 1992 Landers Summary and Take Aways For More Information ch. 22 Weather Research and Forecasting (WRF) WRF Overview WRF Execution Profile: Relatively Flat History of WRF on Intel Many Core (Intel Xeon Phi Product Line) Our Early Experiences with WRF on Knights Landing Compiling WRF for Intel Xeon and Intel Xeon Phi Systems WRF CONUS12km Benchmark Performance MCDRAM Bandwidth Vectorization: Boost of AVX 512 Over AVX2 Core Scaling Summary For More Information ch. 23 N Body simulation. Note continued: Explore AVX 512 Code Characteristics Without AVX 512 Hardware Example Analysis of a Computational Chemistry Code Summary For More Information ch. 11 Vectorization with SDLT What Is SDLT Getting Started SDLT Basics Example Normalizing 3d Points with SIMD What Is Wrong with AOS Memory Layout and SIMD SIMD Prefers Unit Stride Memory Accesses Alpha Blended Overlay Reference Alpha Blended Overlay With SDLT Additional Features Summary For More Information ch. 12 Vectorization with AVX 512 Intrinsics What Are Intrinsics AVX 512 Overview Migrating From Knights Corner AVX 512 Detection Learning AVX 512 Instructions Learning AVX 512 Intrinsics Step by Step Example Using AVX 512 Intrinsics Results Using Our Intrinsics Code For More Information ch. 13 Performance Libraries Intel Performance Library Overview Intel Math Kernel Library Overview.