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DTSTAMP:20250626T233527Z
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UID:submissions.supercomputing.org_SC24_sess533_post284@linklings.com
SUMMARY:Improving SpGEMM Performance Through Reordering and Cluster-Wise C
 omputation
DESCRIPTION:Abdullah Al Raqibul Islam (University of North Carolina at Cha
 rlotte), Helen Xu (Georgia Institute of Technology), Dong Dai (University 
 of Delaware), and Aydin Buluç (Lawrence Berkeley National Laboratory (LBNL
 ))\n\nSparse Matrix-Matrix Multiplication (SpGEMM) is a key kernel in many
  scientific applications and graph workloads. SpGEMM is known to suffer fr
 om poor performance due to irregular memory access patterns. Gustavson's a
 lgorithm, a traditional approach for SpGEMM, involves row/column-wise oper
 ations, facing challenges with irregular accesses to the second matrix. Ou
 r research focuses on enhancing memory locality through matrix reordering 
 and cluster-wise computation to address this issue.\n\nIn this study, we e
 valuate the effect of 10 different reordering algorithms on SpGEMM perform
 ance. Then, we introduce a novel method that employs cluster-wise SpGEMM, 
 merging similar rows into clusters. Our findings show that matrix reorderi
 ng can improve SpGEMM performance by up to 2.3×, and our cluster-wise appr
 oach can further enhance performance by up to 30%.\n\nRegistration Categor
 y: Tech Program Reg Pass, Exhibits Reg Pass\n\nSession Chairs: Ayesha Afza
 l (Friedrich-Alexander University, Erlangen-Nuremberg; Erlangen National H
 igh Performance Computing Center); Sally Ellingson (University of Kentucky
 ); and Alan Sussman (University of Maryland)\n\n
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