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UID:submissions.supercomputing.org_SC23_sess158_pap249@linklings.com
SUMMARY:DASP: Specific Dense Matrix Multiply-Accumulate Units Accelerated 
 General Sparse Matrix-Vector Multiplication
DESCRIPTION:Yuechen Lu and Weifeng Liu (China University of Petroleum, Bei
 jing)\n\nSparse matrix-vector multiplication (SpMV) plays a key role in co
 mputational science and engineering, graph processing and machine learning
  applications. Much SpMV work was devoted to resolving problems such as ra
 ndom access to vector x and unbalanced load. However, we have experimental
 ly found that the compute of inner products still occupies much overhead i
 n SpMV operation, which has been largely ignored in existing work.\n\nIn t
 his paper, we propose DASP, a new algorithm using specific dense MMA units
  for accelerating the compute part of general SpMV. We analyze the row-wis
 e distribution of nonzeros and group the rows into three categories. We th
 en organize them into small blocks of proper sizes to meet the requirement
  of MMA computation. For the three categories, DASP offers different strat
 egies to complete SpMV. The experimental results on the latest NVIDIA Ampe
 re and Hopper GPUs show that our DASP brought significant speedups over st
 ate-of-the-art SpMV work.\n\nTag: Algorithms, Linear Algebra, Post-Moore C
 omputing\n\nRegistration Category: Tech Program Reg Pass\n\nReproducibilit
 y Badges: Artifact Available, Artifact Functional, Results Reproduced\n\nS
 ession Chair: Julien Langou (University of Colorado Denver; University of 
 Colorado, Department of Mathematical and Statistical Sciences)\n\n
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