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SCHEDULE: NOV 16-21, 2014

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Fast Sparse Matrix-Vector Multiplication on GPUs for Graph Applications

SESSION: Numerical Kernels


TIME: 11:00AM - 11:30AM


AUTHOR(S):Arash Ashari, Naser Sedaghati, John Eisenlohr, Srinivasan Parthasarathy, P. Sadayappan



Sparse matrix-vector multiplication (SpMV) is a widely used
computational kernel. In this paper, we present ACSR, an adaptive SpMV algorithm that uses
the standard CSR format but reduces thread divergence by combining
rows into groups (bins) which have a similar number of non-zero
elements. Further, for rows in bins that span a wide range of non
zero counts, dynamic parallelism is leveraged. A significant benefit
of ACSR over other proposed SpMV approaches is that it works directly with the
standard CSR format, and thus avoids significant pre-processing
overheads. A CUDA implementation of ACSR is shown to outperform SpMV
implementations in the NVIDIA CUSP and cuSPARSE libraries on a set of
sparse matrices representing power-law graphs. We also demonstrate the
use of ACSR for the analysis of dynamic graphs, where the improvement
over extant approaches is even higher.

Chair/Author Details:

Kirk E. Jordan (Chair) - IBM Corporation

Arash Ashari - Ohio State University

Naser Sedaghati - Ohio State University

John Eisenlohr - Ohio State University

Srinivasan Parthasarathy - Ohio State University

P. Sadayappan - Ohio State University

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Paper also available from IEEE Computer Society