Solving sparse linear systems on NVIDIA tesla GPUs

Mingliang Wang, Hector Klie, Manish Parashar, Hari Sudan

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

24 Scopus citations

Abstract

Current many-core GPUs have enormous processing power, and unlocking this power for general-purpose computing is very attractive due to their low cost and efficient power utilization. However, the fine-grained parallelism and the stream-programming model supported by these GPUs require a paradigm shift, especially for algorithm designers. In this paper we present the design of a GPU-based sparse linear solver using the Generalized Minimum RESidual (GMRES) algorithm in the CUDA programming environment. Our implementation achieved a speedup of over 20x on the Tesla T10P based GTX280 GPU card for benchmarks with from a few thousands to a few millions unknowns.

Original languageEnglish (US)
Title of host publicationComputational Science - ICCS 2009 - 9th International Conference, Proceedings
Pages864-873
Number of pages10
EditionPART 1
DOIs
StatePublished - 2009
Event9th International Conference on Computational Science, ICCS 2009 - Baton Rouge, LA, United States
Duration: May 25 2009May 27 2009

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 1
Volume5544 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other9th International Conference on Computational Science, ICCS 2009
Country/TerritoryUnited States
CityBaton Rouge, LA
Period5/25/095/27/09

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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