A Dynamic Parameter Tuning Method for High Performance SpMM

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Sparse matrix-matrix multiplication (SpMM) is a basic kernel that is used by many algorithms. Several researches focus on various optimizations for SpMM parallel execution. However, a division of a task for parallelization is not well considered yet. Generally, a matrix is equally divided into blocks for processes even though the sparsities of input matrices are different. The parameter that divides a task into multiple processes for parallelization is fixed. As a result, load imbalance among the processes occurs. To balance the loads among the processes, this paper proposes a dynamic parameter tuning method by analyzing the sparsities of input matrices. The experimental results show that the proposed method improves the performance of SpMM for examined matrices by up to 39.5% and 12.3% on average.

Original languageEnglish
Title of host publicationParallel and Distributed Computing, Applications and Technologies - 21st International Conference, PDCAT 2020, Proceedings
EditorsYong Zhang, Yicheng Xu, Hui Tian
PublisherSpringer Science and Business Media Deutschland GmbH
Pages318-329
Number of pages12
ISBN (Print)9783030692438
DOIs
Publication statusPublished - 2021
Event21st International Conference on Parallel and Distributed Computing, Applications, and Technologies, PDCAT 2020 - Shenzhen, China
Duration: 2020 Dec 282020 Dec 30

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12606 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference21st International Conference on Parallel and Distributed Computing, Applications, and Technologies, PDCAT 2020
Country/TerritoryChina
CityShenzhen
Period20/12/2820/12/30

Keywords

  • Parallel execution
  • Parameter tuning
  • Sparse matrix-matrix multiplication

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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