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| <h1 class="title">MLlib Linear Algebra Acceleration Guide</h1> |
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| <h2 id="introduction">Introduction</h2> |
| |
| <p>This guide provides necessary information to enable accelerated linear algebra processing for Spark MLlib.</p> |
| |
| <p>Spark MLlib defines Vector and Matrix as basic data types for machine learning algorithms. On top of them, <a href="https://en.wikipedia.org/wiki/Basic_Linear_Algebra_Subprograms">BLAS</a> and <a href="https://en.wikipedia.org/wiki/LAPACK">LAPACK</a> operations are implemented and supported by <a href="https://github.com/luhenry/netlib">dev.ludovic.netlib</a> (the algorithms may also call <a href="https://github.com/scalanlp/breeze">Breeze</a>). <code class="language-plaintext highlighter-rouge">dev.ludovic.netlib</code> can use optimized native linear algebra libraries (refered to as “native libraries” or “BLAS libraries” hereafter) for faster numerical processing. <a href="https://software.intel.com/content/www/us/en/develop/tools/math-kernel-library.html">Intel MKL</a> and <a href="http://www.openblas.net">OpenBLAS</a> are two popular ones.</p> |
| |
| <p>The official released Spark binaries don’t contain these native libraries.</p> |
| |
| <p>The following sections describe how to install native libraries, configure them properly, and how to point <code class="language-plaintext highlighter-rouge">dev.ludovic.netlib</code> to these native libraries.</p> |
| |
| <h2 id="install-native-linear-algebra-libraries">Install native linear algebra libraries</h2> |
| |
| <p>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.</p> |
| |
| <h3 id="intel-mkl">Intel MKL</h3> |
| |
| <ul> |
| <li>Download and install Intel MKL. The installation should be done on all nodes of the cluster. We assume the installation location is $MKLROOT (e.g. /opt/intel/mkl).</li> |
| <li>Create soft links to <code class="language-plaintext highlighter-rouge">libmkl_rt.so</code> with specific names in system library search paths. For instance, make sure <code class="language-plaintext highlighter-rouge">/usr/local/lib</code> is in system library search paths and run the following commands: |
| <div class="language-plaintext highlighter-rouge"><div class="highlight"><pre class="highlight"><code>$ 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 |
| </code></pre></div> </div> |
| </li> |
| </ul> |
| |
| <h3 id="openblas">OpenBLAS</h3> |
| |
| <p>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 <code class="language-plaintext highlighter-rouge">apt</code> or <code class="language-plaintext highlighter-rouge">yum</code>.</p> |
| |
| <p>For Debian / Ubuntu:</p> |
| <div class="language-plaintext highlighter-rouge"><div class="highlight"><pre class="highlight"><code>sudo apt-get install libopenblas-base |
| sudo update-alternatives --config libblas.so.3 |
| </code></pre></div></div> |
| <p>For CentOS / RHEL:</p> |
| <div class="language-plaintext highlighter-rouge"><div class="highlight"><pre class="highlight"><code>sudo yum install openblas |
| </code></pre></div></div> |
| |
| <h2 id="check-if-native-libraries-are-enabled-for-mllib">Check if native libraries are enabled for MLlib</h2> |
| |
| <p>To verify native libraries are properly loaded, start <code class="language-plaintext highlighter-rouge">spark-shell</code> and run the following code:</p> |
| <div class="language-plaintext highlighter-rouge"><div class="highlight"><pre class="highlight"><code>scala> import dev.ludovic.netlib.NativeBLAS |
| scala> NativeBLAS.getInstance() |
| </code></pre></div></div> |
| |
| <p>If they are correctly loaded, it should print <code class="language-plaintext highlighter-rouge">dev.ludovic.netlib.NativeBLAS = dev.ludovic.netlib.blas.JNIBLAS@...</code>. Otherwise the warnings should be printed:</p> |
| <div class="language-plaintext highlighter-rouge"><div class="highlight"><pre class="highlight"><code>WARN NativeBLAS: Failed to load implementation from:dev.ludovic.netlib.blas.JNIBLAS |
| java.lang.RuntimeException: Unable to load native implementation |
| at dev.ludovic.netlib.NativeBLAS.getInstance(NativeBLAS.java:44) |
| ... |
| </code></pre></div></div> |
| |
| <p>You can also point <code class="language-plaintext highlighter-rouge">dev.ludovic.netlib</code> to specific libraries names and paths. For example, <code class="language-plaintext highlighter-rouge">-Ddev.ludovic.netlib.blas.nativeLib=libmkl_rt.so</code> or <code class="language-plaintext highlighter-rouge">-Ddev.ludovic.netlib.blas.nativeLibPath=$MKLROOT/lib/intel64/libmkl_rt.so</code> for Intel MKL. You have similar parameters for LAPACK and ARPACK: <code class="language-plaintext highlighter-rouge">-Ddev.ludovic.netlib.lapack.nativeLib=...</code>, <code class="language-plaintext highlighter-rouge">-Ddev.ludovic.netlib.lapack.nativeLibPath=...</code>, <code class="language-plaintext highlighter-rouge">-Ddev.ludovic.netlib.arpack.nativeLib=...</code>, and <code class="language-plaintext highlighter-rouge">-Ddev.ludovic.netlib.arpack.nativeLibPath=...</code>.</p> |
| |
| <p>If native libraries are not properly configured in the system, the Java implementation (javaBLAS) will be used as fallback option.</p> |
| |
| <h2 id="spark-configuration">Spark Configuration</h2> |
| |
| <p>The default behavior of multi-threading in either Intel MKL or OpenBLAS may not be optimal with Spark’s execution model <sup id="fnref:1" role="doc-noteref"><a href="#fn:1" class="footnote" rel="footnote">1</a></sup>.</p> |
| |
| <p>Therefore configuring these native libraries to use a single thread for operations may actually improve performance (see <a href="https://issues.apache.org/jira/browse/SPARK-21305">SPARK-21305</a>). It is usually optimal to match this to the number of <code class="language-plaintext highlighter-rouge">spark.task.cpus</code>, which is <code class="language-plaintext highlighter-rouge">1</code> by default and typically left at <code class="language-plaintext highlighter-rouge">1</code>.</p> |
| |
| <p>You can use the options in <code class="language-plaintext highlighter-rouge">config/spark-env.sh</code> to set thread number for Intel MKL or OpenBLAS:</p> |
| <ul> |
| <li>For Intel MKL: |
| <div class="language-plaintext highlighter-rouge"><div class="highlight"><pre class="highlight"><code>MKL_NUM_THREADS=1 |
| </code></pre></div> </div> |
| </li> |
| <li>For OpenBLAS: |
| <div class="language-plaintext highlighter-rouge"><div class="highlight"><pre class="highlight"><code>OPENBLAS_NUM_THREADS=1 |
| </code></pre></div> </div> |
| </li> |
| </ul> |
| |
| <div class="footnotes" role="doc-endnotes"> |
| <ol> |
| <li id="fn:1" role="doc-endnote"> |
| <p>Please refer to the following resources to understand how to configure the number of threads for these BLAS implementations: <a href="https://software.intel.com/en-us/articles/recommended-settings-for-calling-intel-mkl-routines-from-multi-threaded-applications">Intel MKL</a> or <a href="https://software.intel.com/en-us/onemkl-linux-developer-guide-improving-performance-with-threading">Intel oneMKL</a> and <a href="https://github.com/xianyi/OpenBLAS/wiki/faq#multi-threaded">OpenBLAS</a>. <a href="#fnref:1" class="reversefootnote" role="doc-backlink">↩</a></p> |
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