Novel GPU implementation of Jacobi algorithm for Karhunen-Loève transform of dense matrices
Document Type
Conference Proceeding
Publication Date
11-12-2012
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. © 2012 IEEE.
Identifier
84868525351 (Scopus)
ISBN
[9781467331401]
Publication Title
2012 46th Annual Conference on Information Sciences and Systems Ciss 2012
External Full Text Location
https://doi.org/10.1109/CISS.2012.6310720
Recommended Citation
Torun, Mustafa U.; Yilmaz, Onur; and Akansu, Ali N., "Novel GPU implementation of Jacobi algorithm for Karhunen-Loève transform of dense matrices" (2012). Faculty Publications. 18022.
https://digitalcommons.njit.edu/fac_pubs/18022
