Project: Apache Spark (App Definition and Development / Streaming & Messaging)
File: ml-linalg-guide.md

layout: global title: MLlib Linear Algebra Acceleration Guide displayTitle: MLlib Linear Algebra Acceleration Guide license: | Licensed to the Apache Software Foundation (ASF) under one or more contributor license agreements. See the NOTICE file distributed with this work for additional information regarding copyright ownership. The ASF licenses this file to You under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. This guide provides necessary information to enable accelerated linear algebra processing for Spark MLlib. Spark MLlib defines Vector and Matrix as basic data types for machine learning algorithms. On top of them, and operations are implemented and supported by (the algorithms may also call ). `dev.ludovic.netlib` can use optimized native linear algebra libraries (referred to as "native libraries" or "BLAS libraries" hereafter) for faster numerical processing. and are two popular ones. The official released Spark binaries don't contain these native libraries. The following sections describe how to install native libraries, configure them properly, and how to point `dev.ludovic.netlib` to these native libraries. Intel MKL and OpenBLAS are two popular native linear algebra libraries. You can choose one of them based on your preference. We provide basic instructions as below. Download and install Intel MKL. The installation should be done on all nodes of the cluster. We assume the installation location is $MKLROOT (e.g. /opt/intel/mkl). Create soft links to `libmkl_rt.so` with specific names in system library search paths. For instance, make sure `/usr/local/lib` is in system library search paths and run the following commands: ``` $ ln -sf $MKLROOT/lib/intel64/libmkl_rt.so /usr/local/lib/libblas.so.3 $ ln -sf $MKLROOT/lib/intel64/libmkl_rt.so /usr/local/lib/liblapack.so.3 ``` The installation should be done on all nodes of the cluster. Generic version of OpenBLAS are available with most distributions. You can install it with a distribution package manager like `apt` or `yum`. For Debian / Ubuntu: ``` sudo apt-get install libopenblas-base sudo update-alternatives --config libblas.so.3 ``` For CentOS / RHEL: ``` sudo yum install openblas ``` To verify native libraries are properly loaded, start `spark-shell` and run the following code: ``` scala> import dev.ludovic.netlib.blas.NativeBLAS scala> NativeBLAS.getInstance() ``` If they are correctly loaded, it should print `dev.ludovic.netlib.blas.NativeBLAS = dev.ludovic.netlib.blas.JNIBLAS@...`. Otherwise the warnings should be printed: ``` WARN InstanceBuilder: Failed to load implementation from:dev.ludovic.netlib.blas.JNIBLAS ... java.lang.RuntimeException: Unable to load native implementation at dev.ludovic.netlib.blas.InstanceBuilder.nativeBlas(InstanceBuilder.java:59) at dev.ludovic.netlib.blas.NativeBLAS.getInstance(NativeBLAS.java:31) ... ``` You can also point `dev.ludovic.netlib` to specific libraries names and paths. For example, `-Ddev.ludovic.netlib.blas.nativeLib=libmklrt.so` or `-Ddev.ludovic.netlib.blas.nativeLibPath=$MKLROOT/lib/intel64/libmklrt.so` for Intel MKL. You have similar parameters for LAPACK and ARPACK: `-Ddev.ludovic.netlib.lapack.nativeLib=...`, `-Ddev.ludovic.netlib.lapack.nativeLibPath=...`, `-Ddev.ludovic.netlib.arpack.nativeLib=...`, and `-Ddev.ludovic.netlib.arpack.nativeLibPath=...`. If native libraries are not properly configured in the system, the Java implementation (javaBLAS) will be used as fallback option. The default behavior of multi-threading in either Intel MKL or OpenBLAS may not be optimal with Spark's execution model [^1]. Therefore configuring these native libraries to use a single thread for operations may actually improve performance (see ). It is usually optimal to match this to the number of `spark.task.cpus`, which is `1` by default and typically left at `1`. You can use the options in `config/spark-env.sh` to set thread number for Intel MKL or OpenBLAS: For Intel MKL: ``` MKLNUMTHREADS=1 ``` For OpenBLAS: ``` OPENBLASNUMTHREADS=1 ``` or and .