Paper Info
Title
Optimization of sparse matrix-vector multiplication using reordering techniques on GPUs
Abstract
It is well-known that reordering techniques applied to sparse matrices are common strategies to improve the performance of sparse matrix operations, and particularly, the sparse matrix vector multiplication (SpMV) on CPUs. In this paper, we have evaluated some of the most successful reordering techniques on two different GPUs. In addition, in our study a number of sparse matrix storage formats were considered. Executions for both single and double precision arithmetics were also performed. We have found that SpMV is very sensitive to the application of reordering techniques on GPUs. In particular, several characteristics of the reordered matrices that have a big impact on the SpMV performance have been detected. In most of the cases, reordered matrices outperform the original ones, showing noticeable speedups up to 2.6x. We have also observed that there is no one storage format preferred over the others.
Year
DOI
Venue
2012
10.1016/j.micpro.2011.05.005
Microprocessors and Microsystems - Embedded Hardware Design
Keywords
Field
DocType
sparse matrix storage format,reordering technique,reordered matrix,different gpus,big impact,sparse matrix-vector multiplication,sparse matrix operation,spmv performance,sparse matrix,successful reordering technique,storage format,performance,gpus,sparse matrix vector multiplication,reordering,optimization
Sparse matrix-vector multiplication,Matrix (mathematics),Computer science,Sparse approximation,Parallel computing,Double-precision floating-point format,Sparse matrix
Journal
Volume
Issue
ISSN
36
2
Microprocessors and Microsystems
Citations 
PageRank 
References 
22
0.96
18
Authors
4
Name
Order
Citations
PageRank
Juan C. Pichel18810.62
Francisco F. Rivera217726.17
Marcos Fernández3221.30
Aurelio Rodríguez4221.30