# User Guide

By default, SystemML implements all its matrix operations in Java. This simplifies deployment especially in a distributed environment.

In some cases (such as deep learning), the user might want to use native BLAS rather than SystemML’s internal Java library for performing single-node operations such matrix multiplication, convolution, etc.

To allow SystemML to use native BLAS rather than internal Java library, please set the configuration property sysml.native.blas to auto. Other possible options are: mkl, openblas and none. The first two options will only attempt to use the respective BLAS libraries.

By default, SystemML will first attempt to use Intel MKL (if installed) and then OpenBLAS (if installed). If both Intel MKL and OpenBLAS are not available, SystemML falls back to its internal Java library.

The current version of SystemML only supports BLAS on Linux machines.

## Step 1: Install BLAS

### Option 1: Install Intel MKL

• Linux users will have to extract the downloaded .tgz file, execute install.sh and follow the guided setup.

### Option 2: Install OpenBLAS

The default OpenBLAS (via yum/apt-get) uses its internal threading rather than OpenMP, which can lead to performance degradation when using SystemML. So, instead we recommend that you compile OpenBLAS from the source instead of installing it with yum or apt-get.

The steps to install OpenBLAS v0.2.20:

wget https://github.com/xianyi/OpenBLAS/archive/v0.2.20.tar.gz
tar -xzf v0.2.20.tar.gz
cd OpenBLAS-0.2.20/
make clean
make USE_OPENMP=1
sudo make install
# After installation, you may also want to add /opt/OpenBLAS/lib to your LD_LIBRARY_PATH or java.library.path.


When using OpenBLAS, we also depend on GNU OpenMP (gomp) which will be installed by GCC. To find the location of gomp on your system, please use the command ldconfig -p | grep libgomp. If gomp is available as /lib64/libgomp.so.1 instead of /lib64/libgomp.so, please add a softlink to it:

# Centos/RedHat
sudo yum install gcc-c++
# Ubuntu
sudo apt-get install g++
sudo ln -s /lib64/libgomp.so.1 /lib64/libgomp.so


## Step 2: Provide the location of the native libraries

1. Pass the location of the native libraries using command-line options:

• Spark: --conf spark.executorEnv.LD_LIBRARY_PATH=/path/to/blas-n-other-dependencies
• Java: -Djava.library.path=/path/to/blas-n-other-dependencies
2. Alternatively, you can add the location of the native libraries (i.e. BLAS and other dependencies) to the environment variable LD_LIBRARY_PATH (on Linux). If you want to use SystemML with Spark, please add the following line to spark-env.sh (or to the bash profile).

export LD_LIBRARY_PATH=/path/to/blas-n-other-dependencies

In cloud environment where you may not be able to set LD_LIBRARY_PATH or spark.executorEnv.LD_LIBRARY_PATH before starting spark, you can use set the configuration property sysml.native.blas.directory. For example:

mlCtx.setConfigProperty("sysml.native.blas.directory", "/path/to/blas-n-other-dependencies")


## Step 3: Set configuration property to enable native BLAS

The configuration property sysml.native.blas can be either set in the file SystemML-config.xml or using setConfigProperty method of MLContext or mllearn classes. For example:

mlCtx.setConfigProperty("sysml.native.blas", "openblas")


## Common issues on Linux

• Unable to load gomp.

First make sure if gomp is available on your system.

ldconfig -p | grep  libgomp


If the above command returns no results, then you may have to install gcc. On the other hand, if the above command only returns libgomp with major suffix (such as so.1), then please execute the below command:

sudo ln -s /lib64/libgomp.so.1 /usr/lib64/libgomp.so

• Unable to load mkl_rt.

By default, Intel MKL libraries will be installed in the location /opt/intel/mkl/lib/intel64/. Make sure that this path is accessible to Java as per instructions provided in the above section.

• Unable to load openblas.

By default, OpenBLAS libraries will be installed in the location /opt/OpenBLAS/lib/. Make sure that this path is accessible to Java as per instructions provided in the above section.

• Using OpenBLAS without OpenMP can lead to performance degradation when using SystemML.

You can check if the OpenBLAS on you system is compiled with OpenMP or not using following commands: If you don’t see any output after the second command, then OpenBLAS installed on your system is using its internal threading. In this case, we highly recommend that you reinstall OpenBLAS using the above commands.

$ldconfig -p | grep libopenblas.so libopenblas.so (libc6,x86-64) => /opt/OpenBLAS/lib/libopenblas.so$ ldd /opt/OpenBLAS/lib/libopenblas.so | grep libgomp
libgomp.so.1 => /lib64/libgomp.so.1

• Using MKL can lead to slow performance for convolution instruction.

We noticed that double-precision MKL DNN primitives for convolution instruction is considerably slower than than the corresponding single-precision MKL DNN primitives as of MKL 2017 Update 1. We anticipate that this performance bug will be fixed in the future MKL versions. Until then or until SystemML supports single-precision matrices, we recommend that you use OpenBLAS when using script with conv2d.

Here are the end-to-end runtime performance in seconds of 10 conv2d operations on randomly generated 64 images of size 256 X 256 with sparsity 0.9 and 32 filter of size 5x5 with stride = [1,1] and pad=[1,1].

MKL OpenBLAS
Single-precision, channels=3 5.144 7.918
Double-precision, channels=3 12.599 8.688
Single-precision, channels=32 10.765 21.963
Double-precision, channels=32 71.118 34.881

Setup used in the above experiment:

1. Intel MKL 2017 Update 1, OpenBLAS compiled with GNU OpenMP from source using g++.
2. CPU: Intel(R) Xeon(R) CPU E5-2620 v3 @ 2.40GHz

# Developer Guide

This section describes how to compile shared libraries in the folder src/main/cpp/lib. This is required when the developer makes changes to cpp directory or while validating the source package during the release process.

## Intro to CMake

If you are familiar with cmake, skip this section.

In a regular project with a Makefile, the compiled object files are placed in the same directory as the source. Sometimes we don’t want to pollute the source tree. We might also want to have different binaries for different configurations. For instance, if we want to link a binary with separate libraries. CMake supports out of source tree builds. As an illustration, you can create a directory called “BUILD” and invoke cmake like so : cmake <path/to/source>. The makefile and other config files are placed in this “BUILD” directory. You can now say make and the compiled objects and binary files are created in this directory. You can then create another “BUILD2” directory and repeat the process. You can pass options to cmake as well. In this instance, it might be to specify whether to build with Intel MKL or OpenBLAS. This can be done from the command line with a “-D” appended to it, but more interestingly, it can also be done form a n-curses GUI which is invoked as ccmake <path/to/source>. (You may need to install this separately). Also, the C, C++ compilers and their flags are picked up by cmake when set in standard environment variables. These are respectively CC, CXX, CFLAGS & CXFLAGS. As an example, they may be specified as:

CXX=gcc-6 cmake ..


For this project, I typically make a directory in the cpp folder (this folder) and name it the config I use. For instance, INTEL for Intel MKL and OPENBLAS for OpenBLAS.

• Install g++, OpenBLAS and MKL using the above instructions

• Set JAVA_HOME to JDK.

export JAVA_HOME=<path to JDK 1.8>

• Install cmake
# Centos/RedHat
sudo yum install cmake3
# Ubuntu
sudo apt-get install cmake

• Compile the libs using the below script.
mkdir INTEL && cd INTEL
cmake -DUSE_INTEL_MKL=ON -DCMAKE_BUILD_TYPE=Release -DCMAKE_C_COMPILER=gcc -DCMAKE_CXX_COMPILER=g++ -DCMAKE_CXX_FLAGS="-DUSE_GNU_THREADING -m64" ..
make install
cd ..
mkdir OPENBLAS && cd OPENBLAS
cmake -DUSE_OPEN_BLAS=ON -DCMAKE_BUILD_TYPE=Release -DCMAKE_C_COMPILER=gcc -DCMAKE_CXX_COMPILER=g++ -DCMAKE_CXX_FLAGS="-m64" ..
make install
cd ..
# The below script helps maintain this document as well as avoid accidental inclusion of non-standard dependencies.
./check-dependency-linux-x86_64.sh


The generated library files are placed in src/main/cpp/lib. This location can be changed from the CMakeLists.txt file.

The above script also validates whether additional dependencies have been added while compiling and warns the developer.
The current set of dependencies other than MKL and OpenBLAS, are as follows:

• GNU Standard C++ Library: libstdc++.so.6
• GCC version 4.8 shared support library: libgcc_s.so.1
• The GNU libc libraries: libm.so.6, libdl.so.2, libc.so.6, libpthread.so.0
• GCC OpenMP v3.0 shared support library: libgomp.so.1
• Additional OpenBLAS dependencies: Fortran runtime (libgfortran.so.3) and GCC __float128 shared support library (libquadmath.so.0)

If CMake cannot detect your OpenBLAS installation, set the OpenBLAS_HOME environment variable to the OpenBLAS Home.