NDLI: Parallel formulations of matrix-vector multiplication for matrices with large aspect ratios

Content Provider	IEEE Xplore Digital Library
Author	Lloyd, J.
Copyright Year	1996
Description	Author affiliation: Dept. of Comput., Univ. of Manchester Inst. of Sci. & Technol., UK (Lloyd, J.)
Abstract	Matrix-vector multiplication is an important component of a large number of parallel algorithms. Over the years, many parallel formulations of matrix-vector multiplication have been proposed. However, they all tend to suffer from the some basic problem: while they may perform well for square matrices, or matrices with moderate aspect ratios, their efficiency deteriorates considerably for matrices with large aspect ratios. This paper proposes novel techniques for improving the efficiency of matrix-vector multiplication for matrices with large aspect ratios. The basic approach involves partitioning the matrix and vector over a logical array of processors, which is then embedded in the physical architecture. The dimensions of the logical array are chosen so as to minimise the communication overhead associated with the algorithm. Two popular families of parallel architectures are considered: square meshes with wraparound connections, and hypercubes. Theoretical results show that, for large numbers of processors, and for matrices with large aspect ratios, the new schemes perform significantly better than existing ones.
Starting Page	102
Ending Page	108
File Size	708088
Page Count	7
File Format	PDF
ISBN	0818673761
ISSN	10666172
DOI	10.1109/EMPDP.1996.500575
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1996-01-24
Publisher Place	Portugal
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Sparse matrices Logic arrays Parallel algorithms Concurrent computing Partitioning algorithms Parallel architectures Hypercubes Broadcasting Computer architecture Routing
Content Type	Text
Resource Type	Article

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