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DTSTAMP:20211207T055402Z
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UID:submissions.supercomputing.org_SC21_sess256_drs107@linklings.com
SUMMARY:Intelligent Job Scheduling for Next Generation HPC Systems
DESCRIPTION:Doctoral Showcase, Posters\n\nIntelligent Job Scheduling for N
 ext Generation HPC Systems\n\nFan, Lan, Papka\n\nBoth high performance com
 puting (HPC) infrastructures and applications are undergoing significant c
 hanges. The emerging HPC applications are not only compute-intensive, but 
 also data- and memory-intensive. To meet the diverse workload demands, new
  hardware components, such as GPU and burst buffer, are incorporated into 
 the next generation systems. However, existing HPC job schedulers typicall
 y leverage simple heuristics to schedule jobs. The rapid development in sy
 stem infrastructure and diverse workloads pose serious challenges to the t
 raditional heuristic approaches. We propose an intelligent HPC job schedul
 ing framework to address these emerging challenges. Our research takes adv
 antage of advanced machine learning and optimization techniques to extract
  useful workload- and system-specific information and to further guide the
  framework to make informative scheduling decisions under various system c
 onfiguration and diverse workloads. Our framework consists of three main c
 omponents. The first component is job runtime adjuster, which leverages a 
 machine learning model to improve the accuracy of user-provided job runtim
 e estimates. The second component enhances multi-resource scheduling by ex
 ploring multi-objective genetic algorithm. The third component enables the
  scheduler to automatically learn efficient scheduling policies via reinfo
 rcement learning. Our proposed design demonstrates significant performance
  improvements over the state-of-the-art schedulers under various resources
  and applications settings.\n\nTag: In-Person Only\n\nRegistration Categor
 y: Tech Program Reg Pass, Exhibit Hall Only
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