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DTSTART;TZID=America/Denver:20231113T114400
DTEND;TZID=America/Denver:20231113T120300
UID:submissions.supercomputing.org_SC23_sess441_ws_p3hpc115@linklings.com
SUMMARY:CuPBoP-AMD:  Extending CUDA to AMD Platforms
DESCRIPTION:Jun Chen, Xule Zhou, and Hyesoon Kim (Georgia Institute of Tec
 hnology)\n\nThe proliferation of artificial intelligence applications has 
 underscored the need for increased portability among  graphic processing u
 nits (GPUs) from different vendors. With CUDA as one of the most popular G
 PU programming languages, CuPBoP (CUDA for Parallelized and Broad-range Pr
 ocessors) aims to provide NVIDIA's proprietary CUDA language support to a 
 variety of GPU and CPU platforms by translating CUDA programs at the LLVM/
 NVVM IR level. Our work extends CuPBoP to AMD GPUs as CuPBoP-AMD. CuPBoP-A
 MD is a CUDA translator that translates CUDA programs at NVVM IR level to 
 HIP-compatible IR that can run on AMD GPUs. Currently, CuPBoP-AMD translat
 es a broader range of applications in the Rodinia benchmark suite while ma
 intaining approximately equal performance than the existing state-of-the-a
 rt AMD-developed translator, HIPIFY, without requiring programmer interven
 tion.\n\nTag: Performance Measurement, Modeling, and Tools, Performance Op
 timization\n\nRegistration Category: Workshop Reg Pass\n\nSession Chairs: 
 Judith C. Hill (Lawrence Livermore National Laboratory (LLNL)), CJ Newburn
  (NVIDIA Corporation), Scott J. Parker (Argonne National Laboratory (ANL))
 , and John Pennycook (Intel Corporation)\n\n
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