Novel GPU implementation of Jacobi algorithm for Karhunen-Loève transform of dense matrices

Mustafa U. Torun, Onur Yilmaz, Ali N. Akansu

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

5 Scopus citations

Abstract

Jacobi algorithm for Karhunen-Loève transform of a symmetric real matrix, and its parallel implementation using chess tournament algorithm are revisited in this paper. Impact of memory access patterns and significance of memory coalescing on the performance of the GPU implementation for the parallel Jacobi algorithm are emphasized. Two novel memory access methods for the Jacobi algorithm are proposed. It is shown with simulation results that one of the proposed methods achieves 77.3% computational performance improvement over the traditional GPU methods, and it runs 73.5 times faster than CPU for a dense symmetric square matrix of size 1,024.

Original languageEnglish (US)
Title of host publication2012 46th Annual Conference on Information Sciences and Systems, CISS 2012
DOIs
StatePublished - Nov 12 2012
Event2012 46th Annual Conference on Information Sciences and Systems, CISS 2012 - Princeton, NJ, United States
Duration: Mar 21 2012Mar 23 2012

Publication series

Name2012 46th Annual Conference on Information Sciences and Systems, CISS 2012

Other

Other2012 46th Annual Conference on Information Sciences and Systems, CISS 2012
CountryUnited States
CityPrinceton, NJ
Period3/21/123/23/12

All Science Journal Classification (ASJC) codes

  • Information Systems

Keywords

  • CUDA
  • Eigendecomposition
  • GPU Computing
  • Jacobi Algorithm
  • KLT

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