DocumentCode
693950
Title
The Storage Formats for Accelerating SMVP on a GPU
Author
Jin Tian ; Fei Wu ; Rui Zou ; Guohui Zeng ; Li Gong
Author_Institution
Coll. of Electron. & Electr. Eng., Shanghai Univ. Of Eng. Sci., Shanghai, China
fYear
2013
fDate
14-16 Nov. 2013
Firstpage
513
Lastpage
516
Abstract
This paper aims to study how to choose an effective storage format to accelerate sparse matrix vector product (SMVP) occurring in different numerical methods. We discuss and analyze the storage formats of SMVP which implemented on a GPU. The formats are used for hastening the solution of equations arising from numerical methods. The research in this paper can provide fast selects, which allow low storage space and make memory accesses efficiency, for numerical methods to accelerate SMVP.
Keywords
graphics processing units; sparse matrices; storage management; GPU; SMVP; low storage space; memory access efficiency; numerical methods; sparse matrix vector product; Acceleration; Educational institutions; Graphics processing units; Instruction sets; Memory management; Sparse matrices; Vectors; Graphics Processing Unit (GPU); Sparse Matrix Vector Product (SMVP); Storage Format;
fLanguage
English
Publisher
ieee
Conference_Titel
Business Intelligence and Financial Engineering (BIFE), 2013 Sixth International Conference on
Conference_Location
Hangzhou
Print_ISBN
978-1-4799-4778-2
Type
conf
DOI
10.1109/BIFE.2013.107
Filename
6961189
Link To Document