Title: Fast sparse matrix-vector multiplication on GPUs
Abstract: Scaling up the sparse matrix-vector multiplication kernel on modern Graphics Processing Units (GPU) has been at the heart of numerous studies in both academia and industry. In this article we present a novel non-parametric, self-tunable, approach to data representation for computing this kernel, particularly targeting sparse matrices representing power-law graphs. Using real web graph data, we show how our representation scheme, coupled with a novel tiling algorithm, can yield significant benefits over the current state of the art GPU efforts on a number of core data mining algorithms such as PageRank, HITS and Random Walk with Restart.
Publication Year: 2011
Publication Date: 2011-01-01
Language: en
Type: article
Indexed In: ['crossref']
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Cited By Count: 115
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