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             <h1 class="title">Using SystemML with GPU</h1>


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     </ul>
   </li>
 </ul>

 <p><br /></p>

 <h1 id="user-guide">User Guide</h1>

 <p>To use SystemML on GPUs, please ensure that <a href="https://developer.nvidia.com/cuda-90-download-archive">CUDA 9</a> and
 <a href="https://developer.nvidia.com/cudnn">CuDNN 7</a> is installed on your system.</p>

 <h2 id="python-users">Python users</h2>

 <p>Please install SystemML using pip:
 - For released version: <code>pip install systemml</code>
 - For bleeding edge version: <code>pip install https://sparktc.ibmcloud.com/repo/latest/systemml-1.2.0-SNAPSHOT-python.tar.gz</code></p>

 <p>Then you can use the <code>setGPU(True)</code> method of <a href="http://apache.github.io/systemml/spark-mlcontext-programming-guide.html">MLContext</a> and
 <a href="http://apache.github.io/systemml/beginners-guide-python.html#invoke-systemmls-algorithms">MLLearn</a> APIs to enable the GPU usage.</p>

 <p><code>python
 from systemml.mllearn import Caffe2DML
 lenet = Caffe2DML(spark, solver='lenet_solver.proto', input_shape=(1, 28, 28))
 lenet.setGPU(True)
 </code>
 To skip memory-checking and force all GPU-enabled operations on the GPU, please use the <code>setForceGPU(True)</code> method after <code>setGPU(True)</code> method.</p>

 <p><code>python
 from systemml.mllearn import Caffe2DML
 lenet = Caffe2DML(spark, solver='lenet_solver.proto', input_shape=(1, 28, 28))
 lenet.setGPU(True).setForceGPU(True)
 </code></p>

 <h2 id="command-line-users">Command-line users</h2>

 <p>To enable the GPU backend via command-line, please provide <code>systemml-1.*-extra.jar</code> in the classpath and <code>-gpu</code> flag.</p>

 <p><code>
 spark-submit --jars systemml-1.*-extra.jar SystemML.jar -f myDML.dml -gpu
 </code></p>

 <p>To skip memory-checking and force all GPU-enabled operations on the GPU, please provide <code>force</code> option to the <code>-gpu</code> flag.</p>

 <p><code>
 spark-submit --jars systemml-1.*-extra.jar SystemML.jar -f myDML.dml -gpu force
 </code></p>

 <h2 id="scala-users">Scala users</h2>

 <p>To enable the GPU backend via command-line, please provide <code>systemml-1.*-extra.jar</code> in the classpath and use
 the <code>setGPU(True)</code> method of <a href="http://apache.github.io/systemml/spark-mlcontext-programming-guide.html">MLContext</a> API to enable the GPU usage.</p>

 <p><code>
 spark-shell --jars systemml-1.*-extra.jar,SystemML.jar
 </code></p>

 <h1 id="troubleshooting-guide">Troubleshooting guide</h1>

 <ul>
   <li>If you have older gcc (&lt; 5.0) and if you get <code>libstdc++.so.6: version CXXABI_1.3.8 not found</code> error, please upgrade to gcc v5+.
 On Centos 5, you may have to compile gcc from the source:</li>
 </ul>

 <p><code>
 sudo yum install libmpc-devel mpfr-devel gmp-devel zlib-devel*
 curl ftp://ftp.gnu.org/pub/gnu/gcc/gcc-5.3.0/gcc-5.3.0.tar.bz2 -O
 tar xvfj gcc-5.3.0.tar.bz2
 cd gcc-5.3.0
 ./configure --with-system-zlib --disable-multilib --enable-languages=c,c++
 num_cores=`grep -c ^processor /proc/cpuinfo`
 make -j $num_cores
 sudo make install
 </code></p>

 <h1 id="advanced-configuration">Advanced Configuration</h1>

 <h2 id="using-single-precision">Using single precision</h2>

 <p>By default, SystemML uses double precision to store its matrices in the GPU memory.
 To use single precision, the user needs to set the configuration property &#8216;sysml.floating.point.precision&#8217;
 to &#8216;single&#8217;. However, with exception of BLAS operations, SystemML always performs all CPU operations
 in double precision.</p>

 <h2 id="training-very-deep-network">Training very deep network</h2>

 <h3 id="shadow-buffer">Shadow buffer</h3>
 <p>To train very deep network with double precision, no additional configurations are necessary.
 But to train very deep network with single precision, the user can speed up the eviction by
 using shadow buffer. The fraction of the driver memory to be allocated to the shadow buffer can<br />
 be set by using the configuration property &#8216;sysml.gpu.eviction.shadow.bufferSize&#8217;.
 In the current version, the shadow buffer is currently not guarded by SystemML
 and can potentially lead to OOM if the network is deep as well as wide.</p>

 <h3 id="unified-memory-allocator">Unified memory allocator</h3>

 <p>By default, SystemML uses CUDA&#8217;s memory allocator and performs on-demand eviction
 using the eviction policy set by the configuration property &#8216;sysml.gpu.eviction.policy&#8217;.
 To use CUDA&#8217;s unified memory allocator that performs page-level eviction instead,
 please set the configuration property &#8216;sysml.gpu.memory.allocator&#8217; to &#8216;unified_memory&#8217;.</p>


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	<h1 class="title">Using SystemML with GPU</h1>


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	<li><a href="#user-guide" id="markdown-toc-user-guide">User Guide</a> <ul>
	<li><a href="#python-users" id="markdown-toc-python-users">Python users</a></li>
	<li><a href="#command-line-users" id="markdown-toc-command-line-users">Command-line users</a></li>
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	<li><a href="#using-single-precision" id="markdown-toc-using-single-precision">Using single precision</a></li>
	<li><a href="#training-very-deep-network" id="markdown-toc-training-very-deep-network">Training very deep network</a> <ul>
	<li><a href="#shadow-buffer" id="markdown-toc-shadow-buffer">Shadow buffer</a></li>
	<li><a href="#unified-memory-allocator" id="markdown-toc-unified-memory-allocator">Unified memory allocator</a></li>
	</ul>
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	</ul>
	</li>
	</ul>

	<p><br /></p>

	<h1 id="user-guide">User Guide</h1>

	<p>To use SystemML on GPUs, please ensure that <a href="https://developer.nvidia.com/cuda-90-download-archive">CUDA 9</a> and
	<a href="https://developer.nvidia.com/cudnn">CuDNN 7</a> is installed on your system.</p>

	<h2 id="python-users">Python users</h2>

	<p>Please install SystemML using pip:
	- For released version: <code>pip install systemml</code>
	- For bleeding edge version: <code>pip install https://sparktc.ibmcloud.com/repo/latest/systemml-1.2.0-SNAPSHOT-python.tar.gz</code></p>

	<p>Then you can use the <code>setGPU(True)</code> method of <a href="http://apache.github.io/systemml/spark-mlcontext-programming-guide.html">MLContext</a> and
	<a href="http://apache.github.io/systemml/beginners-guide-python.html#invoke-systemmls-algorithms">MLLearn</a> APIs to enable the GPU usage.</p>

	<p><code>python
	from systemml.mllearn import Caffe2DML
	lenet = Caffe2DML(spark, solver='lenet_solver.proto', input_shape=(1, 28, 28))
	lenet.setGPU(True)
	</code>
	To skip memory-checking and force all GPU-enabled operations on the GPU, please use the <code>setForceGPU(True)</code> method after <code>setGPU(True)</code> method.</p>

	<p><code>python
	from systemml.mllearn import Caffe2DML
	lenet = Caffe2DML(spark, solver='lenet_solver.proto', input_shape=(1, 28, 28))
	lenet.setGPU(True).setForceGPU(True)
	</code></p>

	<h2 id="command-line-users">Command-line users</h2>

	<p>To enable the GPU backend via command-line, please provide <code>systemml-1.*-extra.jar</code> in the classpath and <code>-gpu</code> flag.</p>

	<p><code>
	spark-submit --jars systemml-1.*-extra.jar SystemML.jar -f myDML.dml -gpu
	</code></p>

	<p>To skip memory-checking and force all GPU-enabled operations on the GPU, please provide <code>force</code> option to the <code>-gpu</code> flag.</p>

	<p><code>
	spark-submit --jars systemml-1.*-extra.jar SystemML.jar -f myDML.dml -gpu force
	</code></p>

	<h2 id="scala-users">Scala users</h2>

	<p>To enable the GPU backend via command-line, please provide <code>systemml-1.*-extra.jar</code> in the classpath and use
	the <code>setGPU(True)</code> method of <a href="http://apache.github.io/systemml/spark-mlcontext-programming-guide.html">MLContext</a> API to enable the GPU usage.</p>

	<p><code>
	spark-shell --jars systemml-1.*-extra.jar,SystemML.jar
	</code></p>

	<h1 id="troubleshooting-guide">Troubleshooting guide</h1>

	<ul>
	<li>If you have older gcc (< 5.0) and if you get <code>libstdc++.so.6: version CXXABI_1.3.8 not found</code> error, please upgrade to gcc v5+.
	On Centos 5, you may have to compile gcc from the source:</li>
	</ul>

	<p><code>
	sudo yum install libmpc-devel mpfr-devel gmp-devel zlib-devel*
	curl ftp://ftp.gnu.org/pub/gnu/gcc/gcc-5.3.0/gcc-5.3.0.tar.bz2 -O
	tar xvfj gcc-5.3.0.tar.bz2
	cd gcc-5.3.0
	./configure --with-system-zlib --disable-multilib --enable-languages=c,c++
	num_cores=`grep -c ^processor /proc/cpuinfo`
	make -j $num_cores
	sudo make install
	</code></p>

	<h1 id="advanced-configuration">Advanced Configuration</h1>

	<h2 id="using-single-precision">Using single precision</h2>

	<p>By default, SystemML uses double precision to store its matrices in the GPU memory.
	To use single precision, the user needs to set the configuration property ‘sysml.floating.point.precision’
	to ‘single’. However, with exception of BLAS operations, SystemML always performs all CPU operations
	in double precision.</p>

	<h2 id="training-very-deep-network">Training very deep network</h2>

	<h3 id="shadow-buffer">Shadow buffer</h3>
	<p>To train very deep network with double precision, no additional configurations are necessary.
	But to train very deep network with single precision, the user can speed up the eviction by
	using shadow buffer. The fraction of the driver memory to be allocated to the shadow buffer can<br />
	be set by using the configuration property ‘sysml.gpu.eviction.shadow.bufferSize’.
	In the current version, the shadow buffer is currently not guarded by SystemML
	and can potentially lead to OOM if the network is deep as well as wide.</p>

	<h3 id="unified-memory-allocator">Unified memory allocator</h3>

	<p>By default, SystemML uses CUDA’s memory allocator and performs on-demand eviction
	using the eviction policy set by the configuration property ‘sysml.gpu.eviction.policy’.
	To use CUDA’s unified memory allocator that performs page-level eviction instead,
	please set the configuration property ‘sysml.gpu.memory.allocator’ to ‘unified_memory’.</p>


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