From sparse matrix to optimal GPU CUDA sparse matrix vector product implementation

Ahmed H. El Zein, Alistair P. Rendell

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

8 Citations (Scopus)

Abstract

The CUDA model for GPUs presents the programmer with a plethora of different programming options. These includes different memory types, different memory access methods, and different data types. Identifying which options to use and when is a non-trivial exercise. This paper explores the effect of these different options on the performance of a routine that evaluates sparse matrix vector products. A process for analysing performance and selecting the subset of implementations that perform best is proposed. The potential for mapping sparse matrix attributes to optimal CUDA sparse matrix vector product implementation is discussed.

Original languageEnglish
Title of host publicationCCGrid 2010 - 10th IEEE/ACM International Conference on Cluster, Cloud, and Grid Computing
Pages808-813
Number of pages6
DOIs
Publication statusPublished - 30 Jul 2010
Externally publishedYes
Event10th IEEE/ACM International Symposium on Cluster, Cloud, and Grid Computing, CCGrid 2010 - Melbourne, VIC, Australia
Duration: 17 May 201020 May 2010

Publication series

NameCCGrid 2010 - 10th IEEE/ACM International Conference on Cluster, Cloud, and Grid Computing

Conference

Conference10th IEEE/ACM International Symposium on Cluster, Cloud, and Grid Computing, CCGrid 2010
CountryAustralia
CityMelbourne, VIC
Period17/05/1020/05/10

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  • Cite this

    El Zein, A. H., & Rendell, A. P. (2010). From sparse matrix to optimal GPU CUDA sparse matrix vector product implementation. In CCGrid 2010 - 10th IEEE/ACM International Conference on Cluster, Cloud, and Grid Computing (pp. 808-813). [5493382] (CCGrid 2010 - 10th IEEE/ACM International Conference on Cluster, Cloud, and Grid Computing). https://doi.org/10.1109/CCGRID.2010.81