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UID:submissions.supercomputing.org_SC22_sess275_rpost151@linklings.com
SUMMARY:Predicting Cross-Platform Relative Performance with Deep Generativ
 e Models
DESCRIPTION:Posters, Research Posters\n\nPredicting Cross-Platform Relativ
 e Performance with Deep Generative Models\n\nNichols, Yeom, Bhatele\n\nApp
 lications can experience significant performance differences when run on d
 ifferent architectures. For example, GPUs are often utilized to accelerate
  an application over its CPU implementation. Understanding how performance
  changes across platforms is vital to the design of hardware, systems soft
 ware, and performance critical applications. However, modeling the relatio
 nship between systems and performance is difficult as run time data needs 
 to be collected on each platform. In this poster, we present a methodology
  for predicting the relative performance of an application across multiple
  systems using profiled performance counters and deep learning.\n\nRegistr
 ation Category: Tech Program Reg Pass, Exhibits Reg Pass
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