SCHEDULE: NOV 16-22, 2013
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Accelerating Sparse Matrix-Vector Multiplication on GPUs using Bit-Representation-Optimized Schemes
SESSION: Optimizing Numerical Code
EVENT TYPE: Papers
TIME: 4:00PM - 4:30PM
SESSION CHAIR: Naoya Maruyama
AUTHOR(S):Wai Teng Tang, Wen Jun Tan, Rajarshi Ray, Yi Wen Wong, Weiguang Chen, Shyh-hao Kuo, Rick Siow Mong Goh, Stephen John Turner, Weng-Fai Wong
ROOM:401/402/403
ABSTRACT:
The sparse matrix-vector (SpMV) multiplication routine is an important building block used in many iterative algorithms for solving scientific and engineering problems. One of the main challenges of SpMV is its memory-boundedness. Although compression has been proposed previously to improve SpMV performance on CPUs, its use has not been demonstrated on the GPU because of the serial nature of many compression and decompression schemes. In this paper, we introduce a family of bit-representation-optimized (BRO) compression schemes for representing sparse matrices on GPUs. The proposed schemes, BRO-ELL, BRO-COO, and BRO-HYB, perform compression on index data and help to speed up SpMV on GPUs through reduction of memory traffic. Furthermore, we formulate a BRO-aware matrix reordering scheme as a data clustering problem and use it to increase compression ratios. With the proposed schemes, experiments show that average speedups of 1.5X compared to ELLPACK and HYB can be achieved for SpMV on GPUs.
Chair/Author Details:
Naoya Maruyama (Chair) - RIKEN Advanced Institute for Computational Science
Wai Teng Tang - Nanyang Technological University
Wen Jun Tan - Nanyang Technological University
Rajarshi Ray - National University of Singapore
Yi Wen Wong - National University of Singapore
Weiguang Chen - National University of Singapore
Shyh-hao Kuo - Agency for Science, Technology and Research
Rick Siow Mong Goh - Agency for Science, Technology and Research
Stephen John Turner - Nanyang Technological University
Weng-Fai Wong - National University of Singapore
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