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<p class="MsoNormal"><span class="contentpasted0"><span style="font-family:"Aptos",sans-serif;color:#242424;background:white">The High-Performance Deep Learning (HiDL) team is pleased to announce</span></span><span style="font-size:12.0pt;font-family:"Aptos",sans-serif;color:black"><o:p></o:p></span></p>
<p class="MsoNormal" style="background:white"><span class="contentpasted0"><span style="font-family:"Aptos",sans-serif;color:#242424;background:white">the release of </span></span><span class="markw2t7fp904"><span style="font-family:"Aptos",sans-serif;color:#242424;background:white">MPI4DL</span></span><span class="contentpasted0"><span style="font-family:"Aptos",sans-serif;color:#242424;background:white"> 0.6,
which is </span></span><span class="contentpasted0"><span style="font-family:"Aptos",sans-serif;color:black">a distributed and accelerated training </span></span><span style="font-size:12.0pt;font-family:"Aptos",sans-serif;color:black"><o:p></o:p></span></p>
<p class="MsoNormal" style="background:white"><span class="contentpasted0"><span style="font-family:"Aptos",sans-serif;color:black">framework for very high-resolution images that integrates Spatial Parallelism, </span></span><span style="font-size:12.0pt;font-family:"Aptos",sans-serif;color:black"><o:p></o:p></span></p>
<p class="MsoNormal" style="background:white"><span class="contentpasted0"><span style="font-family:"Aptos",sans-serif;color:black">Layer Parallelism, Bidirectional Parallelism, and Pipeline Parallelism </span></span><span class="contentpasted0"><span style="font-family:"Aptos",sans-serif;color:#242424">with
<o:p></o:p></span></span></p>
<p class="MsoNormal" style="background:white"><span class="contentpasted0"><span style="font-family:"Aptos",sans-serif;color:#242424">support for the MVAPICH2-GDR high-performance CUDA-aware <o:p></o:p></span></span></p>
<p class="MsoNormal" style="background:white"><span class="contentpasted0"><span style="font-family:"Aptos",sans-serif;color:#242424">communication backend. <o:p></o:p></span></span></p>
<p class="MsoNormal" style="background:white"><span class="contentpasted0"><span style="font-family:"Aptos",sans-serif;color:#242424"><o:p> </o:p></span></span></p>
<p class="MsoNormal" style="background:white"><span class="contentpasted0"><span style="font-family:"Aptos",sans-serif;color:#242424">This library allows MPI-driven converged software infrastructure to extract
<o:p></o:p></span></span></p>
<p class="MsoNormal" style="background:white"><span class="contentpasted0"><span style="font-family:"Aptos",sans-serif;color:#242424">maximum performance and scalability for AI, Big Data and Data Science
<o:p></o:p></span></span></p>
<p class="MsoNormal" style="background:white"><span class="contentpasted0"><span style="font-family:"Aptos",sans-serif;color:#242424">applications and workflows on modern heterogeneous clusters consisting
<o:p></o:p></span></span></p>
<p class="MsoNormal" style="background:white"><span class="contentpasted0"><span style="font-family:"Aptos",sans-serif;color:#242424">of diverse CPUs, GPUs, and Interconnects (InfiniBand, ROCE, Omni-Path, iWARP,
<o:p></o:p></span></span></p>
<p class="MsoNormal" style="background:white"><span class="contentpasted0"><span style="font-family:"Aptos",sans-serif;color:#242424">and SlingShot).</span></span><span style="font-family:"Aptos",sans-serif;color:#242424"><o:p></o:p></span></p>
<p class="MsoNormal" style="background:white"><span style="font-size:12.0pt;font-family:"Aptos",sans-serif;color:black"><o:p> </o:p></span></p>
<p class="MsoNormal" style="background:white"><span style="color:black">The new features available with this release of the MPI4DL package are as follows:</span><o:p></o:p></p>
<p class="MsoNormal" style="background:white"><o:p> </o:p></p>
<p class="MsoNormal" style="background:white"><span class="contentpasted0"><span style="font-family:"Aptos",sans-serif;color:#242424;background:white">* </span></span><span class="markw2t7fp904"><span style="font-family:"Aptos",sans-serif;color:#242424;background:white">MPI4DL</span></span><span class="contentpasted0"><span style="font-family:"Aptos",sans-serif;color:#242424;background:white"> 0.6:</span></span><span style="font-size:12.0pt;font-family:"Aptos",sans-serif;color:black"><o:p></o:p></span></p>
<ul type="disc">
<li class="contentpasted1" style="color:#333333;text-align:justify;mso-list:l1 level1 lfo2;background:white">
<span style="font-size:10.5pt;font-family:"Helvetica",sans-serif">Based on PyTorch<o:p></o:p></span></li><li class="contentpasted1" style="color:#333333;text-align:justify;mso-list:l1 level1 lfo2;background:white;box-sizing: border-box">
<span style="font-size:10.5pt;font-family:"Helvetica",sans-serif;color:red">(NEW)<span class="contentpasted11"> </span></span><span style="font-size:10.5pt;font-family:"Helvetica",sans-serif">Support for training very high-resolution images<o:p></o:p></span></li><ul type="circle">
<li class="contentpasted1" style="color:#333333;text-align:justify;mso-list:l1 level2 lfo2;background:white;box-sizing:border-box">
<span style="font-size:10.5pt;font-family:"Helvetica",sans-serif">Distributed training support for:<o:p></o:p></span></li><ul type="square">
<li class="contentpasted1" style="color:#333333;text-align:justify;mso-list:l1 level3 lfo2;background:white;box-sizing:border-box">
<span style="font-size:10.5pt;font-family:"Helvetica",sans-serif">Layer Parallelism (LP)<o:p></o:p></span></li><li class="contentpasted1" style="color:#333333;text-align:justify;mso-list:l1 level3 lfo2;background:white;box-sizing:border-box">
<span style="font-size:10.5pt;font-family:"Helvetica",sans-serif">Pipeline Parallelism (PP)<o:p></o:p></span></li><li class="contentpasted1" style="color:#333333;text-align:justify;mso-list:l1 level3 lfo2;background:white;box-sizing:border-box">
<span style="font-size:10.5pt;font-family:"Helvetica",sans-serif">Spatial Parallelism (SP)<o:p></o:p></span></li><li class="contentpasted1" style="color:#333333;text-align:justify;mso-list:l1 level3 lfo2;background:white;box-sizing:border-box">
<span style="font-size:10.5pt;font-family:"Helvetica",sans-serif">Spatial and Layer Parallelism (SP+LP)<o:p></o:p></span></li><li class="contentpasted1" style="color:#333333;text-align:justify;mso-list:l1 level3 lfo2;background:white;box-sizing:border-box">
<span style="font-size:10.5pt;font-family:"Helvetica",sans-serif">Spatial and Pipeline Parallelism (SP+PP)<o:p></o:p></span></li><li class="contentpasted1" style="color:#333333;text-align:justify;mso-list:l1 level3 lfo2;background:white;box-sizing:border-box">
<span style="font-size:10.5pt;font-family:"Helvetica",sans-serif;color:red">(NEW)<span class="contentpasted11"> </span></span><span style="font-size:10.5pt;font-family:"Helvetica",sans-serif">Bidirectional and Layer Parallelism (GEMS+LP)<o:p></o:p></span></li><li class="contentpasted1" style="color:#333333;text-align:justify;mso-list:l1 level3 lfo2;background:white;box-sizing:border-box">
<span style="font-size:10.5pt;font-family:"Helvetica",sans-serif;color:red">(NEW)<span class="contentpasted11"> </span></span><span style="font-size:10.5pt;font-family:"Helvetica",sans-serif">Bidirectional and Pipeline Parallelism (GEMS+PP)<o:p></o:p></span></li><li class="contentpasted1" style="color:#333333;text-align:justify;mso-list:l1 level3 lfo2;background:white;box-sizing:border-box">
<span style="font-size:10.5pt;font-family:"Helvetica",sans-serif;color:red">(NEW)<span class="contentpasted11"> </span></span><span style="font-size:10.5pt;font-family:"Helvetica",sans-serif">Spatial, Bidirectional and Layer Parallelism (SP+GEMS+LP)<o:p></o:p></span></li><li class="contentpasted1" style="color:#333333;text-align:justify;mso-list:l1 level3 lfo2;background:white;box-sizing:border-box">
<span style="font-size:10.5pt;font-family:"Helvetica",sans-serif;color:red">(NEW)<span class="contentpasted11"> </span></span><span style="font-size:10.5pt;font-family:"Helvetica",sans-serif">Spatial, Bidirectional and Pipeline Parallelism (SP+GEMS+PP)<o:p></o:p></span></li></ul>
<li class="contentpasted1" style="color:#333333;text-align:justify;mso-list:l1 level2 lfo2;background:white;box-sizing:border-box">
<span style="font-size:10.5pt;font-family:"Helvetica",sans-serif;color:red">(NEW)<span class="contentpasted11"> </span></span><span style="font-size:10.5pt;font-family:"Helvetica",sans-serif">Support for AmoebaNet and ResNet models<o:p></o:p></span></li><li class="contentpasted1" style="color:#333333;text-align:justify;mso-list:l1 level2 lfo2;background:white;box-sizing:border-box">
<span style="font-size:10.5pt;font-family:"Helvetica",sans-serif;color:red">(NEW)<span class="contentpasted11"> </span></span><span style="font-size:10.5pt;font-family:"Helvetica",sans-serif">Support for different image sizes and custom datasets<o:p></o:p></span></li></ul>
<li class="contentpasted1" style="color:#333333;text-align:justify;mso-list:l1 level1 lfo2;background:white;box-sizing: border-box">
<span style="font-size:10.5pt;font-family:"Helvetica",sans-serif">Exploits collective features of MVAPICH2-GDR<o:p></o:p></span></li><li class="contentpasted1" style="color:#333333;text-align:justify;mso-list:l1 level1 lfo2;background:white;box-sizing: border-box">
<span style="font-size:10.5pt;font-family:"Helvetica",sans-serif">Compatible with<o:p></o:p></span></li><ul type="circle">
<li class="contentpasted1" style="color:#333333;text-align:justify;mso-list:l1 level2 lfo2;background:white;box-sizing:border-box">
<span style="font-size:10.5pt;font-family:"Helvetica",sans-serif">NVIDIA GPU A100 and V100<o:p></o:p></span></li><li class="contentpasted1" style="color:#333333;text-align:justify;mso-list:l1 level2 lfo2;background:white;box-sizing:border-box">
<span style="font-size:10.5pt;font-family:"Helvetica",sans-serif">CUDA [11.6, 11.7]<o:p></o:p></span></li><li class="contentpasted1" style="color:#333333;text-align:justify;mso-list:l1 level2 lfo2;background:white;box-sizing:border-box">
<span style="font-size:10.5pt;font-family:"Helvetica",sans-serif">Python >= 3.8<o:p></o:p></span></li><li class="contentpasted1" style="color:#333333;text-align:justify;mso-list:l1 level2 lfo2;background:white;box-sizing:border-box">
<span style="font-size:10.5pt;font-family:"Helvetica",sans-serif">PyTorch [1.12.1 , 1.13.1]<o:p></o:p></span></li><li class="contentpasted1" style="color:#333333;text-align:justify;mso-list:l1 level2 lfo2;background:white;box-sizing:border-box">
<span style="font-size:10.5pt;font-family:"Helvetica",sans-serif">MVAPICH2-GDR = 2.3.7
<o:p></o:p></span></li></ul>
</ul>
<p class="MsoNormal" style="background:white"><span class="contentpasted0"><span style="font-family:"Aptos",sans-serif;color:#242424;background:white">The </span></span><span class="markw2t7fp904"><span style="font-family:"Aptos",sans-serif;color:#242424;background:white">MPI4DL</span></span><span class="contentpasted0"><span style="font-family:"Aptos",sans-serif;color:#242424;background:white"> package
is open-source, and hosted at the following URL:</span></span><span style="font-size:12.0pt;font-family:"Aptos",sans-serif;color:black"><o:p></o:p></span></p>
<p class="MsoNormal" style="background:white"><span style="font-family:"Aptos",sans-serif;color:#242424;background:white"><br>
<span class="contentpasted0"><a href="https://github.com/OSU-Nowlab/MPI4DL" target="_blank">https://github.com/OSU-Nowlab/<span class="markw2t7fp904"><span style="color:blue">MPI4DL</span></span></a>. </span></span><span style="font-size:12.0pt;font-family:"Aptos",sans-serif;color:black"><o:p></o:p></span></p>
<p class="MsoNormal" style="background:white"><span style="font-size:11.5pt;font-family:"Segoe UI",sans-serif;color:#242424"><o:p> </o:p></span></p>
<p class="MsoNormal" style="background:white"><span class="contentpasted0"><span style="font-family:"Segoe UI",sans-serif;color:#242424">For associated release information, please visit the following URL:</span></span><span style="font-size:11.5pt;font-family:"Segoe UI",sans-serif;color:#242424"><o:p></o:p></span></p>
<p class="MsoNormal" style="background:white"><span style="font-size:11.5pt;font-family:"Segoe UI",sans-serif;color:#242424"><o:p> </o:p></span></p>
<p class="MsoNormal" style="margin-bottom:12.0pt;background:white"><span style="font-size:12.0pt;font-family:"Aptos",sans-serif;color:black"><a href="http://hidl.cse.ohio-state.edu/" target="_blank"><span style="font-size:11.0pt;background:white">http://hidl.cse.ohio-state.edu</span></a><o:p></o:p></span></p>
<p class="MsoNormal" style="background:white"><span class="contentpasted0"><span style="font-family:"Aptos",sans-serif;color:#242424;background:white">Sample performance numbers for </span></span><span class="markw2t7fp904"><span style="font-family:"Aptos",sans-serif;color:#242424;background:white">MPI4DL</span></span><span class="contentpasted0"><span style="font-family:"Aptos",sans-serif;color:#242424;background:white"> using
deep learning</span></span><span style="font-size:12.0pt;font-family:"Aptos",sans-serif;color:black"><o:p></o:p></span></p>
<p class="MsoNormal"><span class="contentpasted0"><span style="font-family:"Aptos",sans-serif;color:#242424;background:white">application benchmarks can be viewed by visiting the `Performance' tab</span></span><span style="font-size:12.0pt;font-family:"Aptos",sans-serif;color:black"><br>
</span><span class="contentpasted0"><span style="font-family:"Aptos",sans-serif;color:#242424;background:white">of the above website.</span></span><span style="font-size:12.0pt;font-family:"Aptos",sans-serif;color:black"><br>
<br>
</span><span class="contentpasted0"><span style="font-family:"Aptos",sans-serif;color:#242424;background:white">All questions, feedback, and bug reports are welcome. Please post to</span></span><span style="font-size:12.0pt;font-family:"Aptos",sans-serif;color:black"><br>
</span><span class="contentpasted0"><span style="font-family:"Aptos",sans-serif;color:#242424;background:white"><a href="mailto:hidl-discuss@lists.osu.edu">hidl-discuss@lists.osu.edu</a>.</span></span><span style="font-size:12.0pt;font-family:"Aptos",sans-serif;color:black"><br>
<br>
</span><span class="contentpasted0"><span style="font-family:"Aptos",sans-serif;color:#242424;background:white">Thanks,</span></span><span style="font-size:12.0pt;font-family:"Aptos",sans-serif;color:black"><br>
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</span><span class="contentpasted0"><span style="font-family:"Aptos",sans-serif;color:#242424;background:white">The High-Performance Deep Learning (HiDL) Team</span></span><span style="font-size:12.0pt;font-family:"Aptos",sans-serif;color:black"><br>
<a href="http://hidl.cse.ohio-state.edu/" target="_blank"><span style="font-size:11.0pt;background:white">http://hidl.cse.ohio-state.edu</span></a><o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-size:12.0pt;font-family:"Aptos",sans-serif;color:black"><o:p> </o:p></span></p>
<p class="MsoNormal"><span style="font-size:12.0pt;font-family:"Aptos",sans-serif;color:black">PS: The number of organizations using the HiDL stacks has crossed 88<o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-size:12.0pt;font-family:"Aptos",sans-serif;color:black">(from 21 countries). The HiDL team would like to thank all its users<o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-size:12.0pt;font-family:"Aptos",sans-serif;color:black">and organizations!!<br>
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