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DTSTART:19700308T020000
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DTSTAMP:20230124T171523Z
LOCATION:D174
DTSTART;TZID=America/Chicago:20221118T112400
DTEND;TZID=America/Chicago:20221118T115100
UID:submissions.supercomputing.org_SC22_sess450_ws_waccpd112@linklings.com
SUMMARY:GPU-Accelerated Sparse Matrix Vector Product Based on Element-by-E
 lement Method for Unstructured FEM Using OpenACC
DESCRIPTION:Workshop\n\nGPU-Accelerated Sparse Matrix Vector Product Based
  on Element-by-Element Method for Unstructured FEM Using OpenACC\n\nKusaka
 be, Fujita, Ichimura, Hori, Lalith\n\nThe development of directive based p
 arallel programming models such as OpenACC has significantly reduced the c
 ost in using accelerators such as GPUs. In this study, the sparse matrix v
 ector product (SpMV), which is often the most computationally expensive pa
 rt in physics-based simulations, was accelerated by GPU porting using Open
 ACC. Further speed-up was achieved by introducing the element-by-element (
 EBE) method in SpMV, an algorithm that is suitable for GPU architecture be
 cause it requires large amount of operations but small amount of memory ac
 cess. In a comparison on one compute node of the supercomputer ABCI, using
  GPUs resulted in a 21-fold speedup over the CPU-only case, even when usin
 g the typical SpMV algorithm, and an additional 2.9-fold speedup when usin
 g the EBE method. The results on such analysis was applied to a seismic re
 sponse analysis considering soil liquefaction, and using GPUs resulted in 
 a 42-fold speedup compared to using only CPUs.\n\nSession Format: Recorded
 \n\nTag: Accelerator-based Architectures, Compilers, Dataflow and Tasking,
  Directive Based Programming, Heterogeneous Systems, Parallel Programming 
 Languages and Models, Runtime Systems\n\nRegistration Category: Workshop R
 eg Pass
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