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.
Yang et al. present a novel non-parametric and selftunable approach REF to data presentation for computing this kernel, particularly targeting sparse matrices representing power-law graphs.
Fast Sparse Matrix-Vector Multiplication on GPUs: Implications for Graph Mining
Proceedings of the VLDB Endowment (PVLDB), Vol. 4, No. 4, pp. 231-242 (2011)
Mathematics Computer Science