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, self-tunable, approach to data representation for computing this kernel, particularly targeting sparse matrices representing power-law graphs. Using real 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. 
Xintian Yang (Ohio State University), Srinivasan Parthasarathy (Ohio   State University), Ponnuswamy Sadayappan (Ohio State ...
cs.NA cs.MS