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DTSTAMP:20210402T160558Z
LOCATION:Poster Module
DTSTART;TZID=America/New_York:20201118T083000
DTEND;TZID=America/New_York:20201118T170000
UID:submissions.supercomputing.org_SC20_sess340_spostg105@linklings.com
SUMMARY:Using Machine Learning for OpenMP GPU Offloading in LLVM
DESCRIPTION:ACM Student Research Competition: Graduate Poster, ACM Student
  Research Competition: Undergraduate Poster, Posters\n\nUsing Machine Lear
 ning for OpenMP GPU Offloading in LLVM\n\nMishra\n\nOpenMP 5.0 provides fe
 atures to exploit the compute power within the node of today's leadership 
 class facilities. Among these features, the GPU offloading directives are 
 key to take advantage of heterogeneity on modern machines. These features 
 place the domain scientists with portability challenges, however, especial
 ly for optimizing data movement between a host and a device. Tools that fa
 cilitate the usage of such features are becoming important to the scientif
 ic community. An important tool for porting legacy codes to newer machines
  will be compilers that can predict the feasibility of transferring kernel
 s on GPUs and insert required OpenMP GPU offload features automatically at
  compile time. In this work, we are exploring a novel approach for the aut
 omated handling of OpenMP GPU offloading using machine learning techniques
 . We aim to develop an end-to-end application framework, from legacy code 
 to GPU offloading, that integrates machine learning techniques into the LL
 VM compiler.\n\nTag: Student Program\n\nRegistration Category: Tech Progra
 m Reg Pass, Exhibits Reg Pass
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