On the Representation and Multiplication of Hypersparse Matrices

On the Representation and Multiplication of Hypersparse Matrices (2008)

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by Aydın Buluç , John R. Gilbert

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Datum	Value	Source
TITLE	On the Representation and Multiplication of Hypersparse Matrices	user correction
AUTHOR NAME	Aydın Buluç	user correction
AUTHOR AFFIL	Department of Computer Science, University of California	user correction
AUTHOR ADDR	Santa Barbara, CA	user correction
AUTHOR NAME	John R. Gilbert	user correction
AUTHOR AFFIL	Department of Computer Science, University of California	user correction
AUTHOR ADDR	Santa Barbara, CA	user correction
ABSTRACT	Multicore processors are marking the beginning of a new era of computing where massive parallelism is available and necessary. Slightly slower but easy to parallelize kernels are becoming more valuable than sequentially faster kernels that are unscalable when parallelized. In this paper, we focus on the multiplication of sparse matrices (SpGEMM). We first present the issues with existing sparse matrix representations and multiplication algorithms that make them unscalable to thousands of processors. Then, we develop and analyze two new algorithms that overcome these limitations. We consider our algorithms first as the sequential kernel of a scalable parallel sparse matrix multiplication algorithm and second as part of a polyalgorithm for SpGEMM that would execute different kernels depending on the sparsity of the input matrices. Such a sequential kernel requires a new data structure that exploits the hypersparsity of the individual submatrices owned by a single processor after the 2D partitioning. We experimentally evaluate the performance and characteristics of our algorithms and show that they scale significantly better than existing kernels.	user correction
YEAR	2008	user correction
CITATIONS	30 found	ParsCit 1.0