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<p class="MsoNormal"><b>AI for Datacenter Optimization (ADOPT)<o:p></o:p></b></p>
<p class="MsoNormal"> <o:p></o:p></p>
<p class="MsoNormal">In conjunction with the <a href="http://www.ipdps.org/ipdps2022"><b>IEEE International Parallel and Distributed Processing Symposium</b></a><o:p></o:p></p>
<p class="MsoNormal"><o:p> </o:p></p>
<p class="MsoNormal">Workshop website : <a href="https://dcc.mit.edu/workshop">https://dcc.mit.edu/workshop</a>
<o:p></o:p></p>
<p class="MsoNormal"><o:p> </o:p></p>
<p class="MsoNormal">Artificial intelligence (AI) and Machine learning (ML) workloads are an increasingly larger share of the compute workloads in traditional High-Performance Computing (HPC) centers and commercial cloud systems. This has led to a new focus
on approaches to optimized resource usage, allocations and deployment of new AI frameworks. With these changes, there is a need to better understand HPC/cloud/datacenter operations with the goal of developing improved scheduling policies, identifying inefficiencies
in resource utilization, energy/power consumption, failure prediction, as well as identifying policy violations. Simultaneously, there is a growing interest in addressing the increasing power requirements for AI training and inference operations in HPC/Cloud
environments with the goal of minimizing the accompanying effects on climate change. There are several publicly available datasets such as the Argonne Leadership Computing Facility dataset, Blue Waters System Monitoring dataset, Philly traces by Microsoft
Inc., the Google Cluster Usage traces and the Atlas cluster trace repository that can enable this research. In addition, the recently released MIT Supercloud Dataset includes monitoring logs from the MIT Supercloud system which include time series of CPU and
GPU usage by jobs, memory usage, file system logs, and physical monitoring data. This workshop will focus on AI/ML approaches to datacenter operations, power/energy modeling, scheduling, optimization and monitoring tools. In addition to these topics, this
workshop solicits papers that address any of the challenges in enabling large-scale AI in HPC/Cloud environments, including:<o:p></o:p></p>
<ul style="margin-top:0in" type="disc">
<li class="MsoNormal" style="mso-list:l0 level1 lfo1">Operational insights from deploying and scaling AI/ML workloads in shared systems<o:p></o:p></li><li class="MsoNormal" style="mso-list:l0 level1 lfo1">Parallelization strategies for AI training<o:p></o:p></li><li class="MsoNormal" style="mso-list:l0 level1 lfo1">Optimization strategies including algorithmic and hardware innovations for faster training and inference <o:p></o:p></li><li class="MsoNormal" style="mso-list:l0 level1 lfo1">Scheduling strategies for AI and traditional HPC workloads in large scale environments<o:p></o:p></li><li class="MsoNormal" style="mso-list:l0 level1 lfo1">Approaches and instrumentation for measurement of the climate impact of AI<o:p></o:p></li></ul>
<p class="MsoNormal"><b><o:p> </o:p></b></p>
<p class="MsoNormal"><b>Submission deadlines: </b><o:p></o:p></p>
<ul style="margin-top:0in" type="disc">
<li class="MsoNormal" style="mso-list:l1 level1 lfo2">Paper submission - February 21, 2022<o:p></o:p></li><li class="MsoNormal" style="mso-list:l1 level1 lfo2">Author notification - March 1, 2022<o:p></o:p></li><li class="MsoNormal" style="mso-list:l1 level1 lfo2">Camera ready deadline - March 15, 2022 <o:p></o:p></li></ul>
<p class="MsoNormal"><b><o:p> </o:p></b></p>
<p class="MsoNormal"><b>Submission Details: </b>Submissions will be evaluated in a single blind process. Papers must not exceed 8 single-spaced, double-column pages using 10-point font on 8.5x11 inch pages (excluding references). Manuscripts must use IEEE
Conference style available here: <a href="https://www.ieee.org/conferences/publishing/templates.html"><b>https://www.ieee.org/conferences/publishing/templates.html</b></a> . Submitted papers must not be under review at another conference, journal or workshop.
One of the authors of accepted papers is expected to present at the workshop.<o:p></o:p></p>
<p class="MsoNormal"><o:p> </o:p></p>
<p class="MsoNormal">Link for paper submissions : <a href="https://easychair.org/conferences/?conf=adopt22">https://easychair.org/conferences/?conf=adopt22</a>
<o:p></o:p></p>
<p class="MsoNormal"> <o:p></o:p></p>
<p class="MsoNormal"><o:p> </o:p></p>
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