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UID:submissions.supercomputing.org_SC23_sess179_pap501@linklings.com
SUMMARY:HPAC-Offload: Accelerating HPC Applications with Portable Approxim
 ate Computing on the GPU
DESCRIPTION:Zane Fink (Lawrence Livermore National Laboratory (LLNL), Univ
 ersity of Illinois) and Konstantinos Parasyris, Giorgis Georgakoudis, and 
 Harshitha Menon (Lawrence Livermore National Laboratory (LLNL))\n\nThe end
  of Dennard scaling and the slowdown of Moore's law led to a shift in tech
 nology trends toward parallel architectures, particularly in HPC systems. 
 To continue providing performance benefits, HPC should embrace Approximate
  Computing (AC), which trades application quality loss for improved perfor
 mance. However, existing AC techniques have not been extensively applied a
 nd evaluated in state-of-the-art hardware architectures such as GPUs, the 
 primary execution vehicle for HPC applications today.\n\nThis paper presen
 ts HPAC-Offload, a pragma-based programming model that extends OpenMP offl
 oad applications to support AC techniques, allowing portable approximation
 s across different GPU architectures. We conduct a comprehensive performan
 ce analysis of HPAC-Offload across GPU-accelerated HPC applications, revea
 ling that AC techniques can significantly accelerate HPC applications (1.6
 4x LULESH on AMD, 1.57x NVIDIA) with minimal quality loss (0.1%). Our anal
 ysis offers deep insights into the performance of GPU-based AC that guide 
 the future development of AC algorithms and systems for these architecture
 s.\n\nTag: Accelerators, Distributed Computing, Middleware and System Soft
 ware, Performance Measurement, Modeling, and Tools, Post-Moore Computing\n
 \nRegistration Category: Tech Program Reg Pass\n\nAward Finalist: Best Pap
 er Finalist\n\nReproducibility Badges: Artifact Available, Artifact Functi
 onal, Results Reproduced\n\nSession Chair: Hari Subramoni (The Ohio State 
 University)\n\n
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