Analysis of Schedule and Layout Tuning for Sparse Matrices With Compound Entries on GPUs

J. S. Mueller-Roemer*, A. Stork, D. Fellner

*Corresponding author for this work

Research output: Contribution to journalArticle

Abstract

Large sparse matrices with compound entries, i.e. complex and quaternionic matrices as well as matrices with dense blocks, are a core component of many algorithms in geometry processing, physically based animation and other areas of computer graphics. We generalize several matrix layouts and apply joint schedule and layout autotuning to improve the performance of the sparse matrix-vector product on massively parallel graphics processing units. Compared to schedule tuning without layout tuning, we achieve speedups of up to 5.5 ×. In comparison to cuSPARSE, we achieve speedups of up to 4.7 ×.

Original languageEnglish
Pages (from-to)133-143
Number of pages11
JournalComputer Graphics Forum
Volume39
Issue number6
DOIs
Publication statusPublished - 1 Sep 2020

Keywords

  • GPGPU
  • parallel computing
  • sparse matrix
  • SpMV

ASJC Scopus subject areas

  • Computer Graphics and Computer-Aided Design

Fields of Expertise

  • Information, Communication & Computing

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