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A New Approach for Accelerating the Sparse Matrix-Vector Multiplication
Timisoara, Romania September 26-September 29
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/SYNASC.2006.4Eighth International Symposium on Sym ...
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Pavel Tvrdik, Ivan Simecek, "A New Approach for Accelerating the Sparse Matrix-Vector Multiplication," Symbolic and Numeric Algorithms for Scientific Computing, International Symposium on, pp. 156-163, Eighth International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC'06), 2006.
 
 
BibTex
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@article{ 10.1109/SYNASC.2006.4,
author = {Pavel Tvrdik and Ivan Simecek},
title = {A New Approach for Accelerating the Sparse Matrix-Vector Multiplication},
journal ={Symbolic and Numeric Algorithms for Scientific Computing, International Symposium on},
volume = {0},
year = {2006},
isbn = {0-7695-2740-X},
pages = {156-163},
doi = {http://doi.ieeecomputersociety.org/10.1109/SYNASC.2006.4},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
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TY - CONF
JO - Symbolic and Numeric Algorithms for Scientific Computing, International Symposium on
TI - A New Approach for Accelerating the Sparse Matrix-Vector Multiplication
SN - 0-7695-2740-X
SP156
EP163
A1 - Pavel Tvrdik,
A1 - Ivan Simecek,
PY - 2006
KW - null
VL - 0
JA - Symbolic and Numeric Algorithms for Scientific Computing, International Symposium on
ER -
 
Pavel Tvrdik, Czech Technical University, Prague
Ivan Simecek, Czech Technical University, Prague
Sparse matrix-vector multiplication (shortly SpM?V ) is one of most common subroutines in the numerical linear algebra. The problem is that the memory access patterns during the SpM?V are irregular and the utilization of cache can suffer from low spatial or temporal locality.

This paper introduces new approach for the acceleration the SpM?V . This approach consists of 3 steps. The first step divides the whole matrix into smaller parts (regions) those can fit in the cache. The second step improves locality during the multiplication due to better utilization of distant references. The last step maximizes machine computation performance of the partial multiplication for each region.

In this paper, we describe aspects of these 3 steps in more detail (including fast and time-inexpensive algorithms for all steps). Our measurements proved that our approach gives a significant speedup for almost all matrices arising from various technical areas.

Citation:
Pavel Tvrdik, Ivan Simecek, "A New Approach for Accelerating the Sparse Matrix-Vector Multiplication," synasc, pp.156-163, Eighth International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC'06), 2006
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