3207

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

Xintian Yang, Srinivasan Parthasarathy, Ponnuswamy Sadayappan
Department of Computer Science and Engineering, Ohio State University, Columbus, OH 43210
arXiv:1103.2405v1 [cs.DB] (12 Mar 2011), VLDB2011; Proceedings of the VLDB Endowment (PVLDB), Vol. 4, No. 4, pp. 231-242 (2011)

@article{2011arXiv1103.2405Y,

   author={Yang}, X. and {Parthasarathy}, S. and {Sadayappan}, P.},

   title={“{Fast Sparse Matrix-Vector Multiplication on GPUs: Implications for Graph Mining}”},

   journal={ArXiv e-prints},

   archivePrefix={“arXiv”},

   eprint={1103.2405},

   primaryClass={“cs.DB”},

   keywords={Computer Science – Databases},

   year={2011},

   month={mar},

   adsurl={http://adsabs.harvard.edu/abs/2011arXiv1103.2405Y},

   adsnote={Provided by the SAO/NASA Astrophysics Data System}

}

Download Download (PDF)   View View   Source Source   

716

views

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.
No votes yet.
Please wait...

* * *

* * *

HGPU group © 2010-2017 hgpu.org

All rights belong to the respective authors

Contact us: