Fast Sparse Matrix-Vector Multiplication on GPUs: Implications for Graph Mining

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, selftunable, 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.

• Type: Journal
• Year: 2011
• Where: CORR
• Authors: Xintian Yang, Srinivasan Parthasarathy, Ponnuswamy Sadayappan
• Keywords: CORR 2011, Education, Sparse Matrices, Sparse Matrix-vector Multiplication, Web Graph Data