Implementing sparse matrix-vector multiplication on throughput-oriented processors

Sparse matrix-vector multiplication (SpMV) is of singular importance in sparse linear algebra. In contrast to the uniform regularity of dense linear algebra, sparse operations encounter a broad spectrum of matrices ranging from the regular to the highly irregular. Harnessing the tremendous potential...

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Bibliographic Details
Published in:	Proceedings of the Conference on High Performance Computing Networking, Storage and Analysis pp. 1 - 11
Main Authors:	Bell, Nathan, Garland, Michael
Format:	Conference Proceeding
Language:	English
Published:	New York, NY, USA ACM 14.11.2009
Series:	ACM Conferences
Subjects:	Bandwidth Computer systems organization > Architectures > Parallel architectures > Multiple instruction, multiple data Computer systems organization > Dependable and fault-tolerant systems and networks Computing methodologies > Symbolic and algebraic manipulation > Symbolic and algebraic algorithms > Linear algebra algorithms General and reference > Cross-computing tools and techniques > Performance Graphics processing units Hardware Instruction sets Kernel Mathematics of computing > Mathematical analysis > Numerical analysis > Computations on matrices Memory management Networks > Network performance evaluation Optimization Sparse matrices Throughput Vectors
ISBN:	1605587443, 9781605587448
ISSN:	2167-4329
Online Access:	Get full text
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