A dynamic parameter tuning method for SpMM parallel execution

DOI Web Site Web Site 38 References Open Access
  • Bin Qi
    Graduate School of Information Sciences Tohoku University Sendai Japan
  • Kazuhiko Komatsu
    Cyberscience Center Tohoku University Sendai Japan
  • Masayuki Sato
    Graduate School of Information Sciences Tohoku University Sendai Japan
  • Hiroaki Kobayashi
    Graduate School of Information Sciences Tohoku University Sendai Japan

Bibliographic Information

Published
2021-12-09
Resource Type
journal article
Rights Information
  • http://onlinelibrary.wiley.com/termsAndConditions#vor
DOI
  • 10.1002/cpe.6755
Publisher
Wiley

Search this article

Description

<jats:title>Summary</jats:title><jats:p>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 article 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% on a single vector engine and 3.49 <jats:inline-graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="graphic/cpe6755-math-0001.png" xlink:title="urn:x-wiley:cpe:media:cpe6755:cpe6755-math-0001"/> on a single CPU.</jats:p>

Journal

References(38)*help

See more

Related Projects

See more

Details 詳細情報について

Report a problem

Back to top