sponsored byACMIEEE The International Conference for High Performance 
Computing, Networking, Storage and Analysis
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SCHEDULE: NOV 15-20, 2015

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Multi-Level Blocking Optimization for Fast Sparse Matrix Vector Multiplication on GPUs

SESSION: Regular & ACM Student Research Competition Poster Reception

EVENT TYPE: Posters, Receptions, ACM Student Research Competition

EVENT TAG(S): HPC Beginner Friendly, Regular Poster

TIME: 5:15PM - 7:00PM

SESSION CHAIR(S): Michela Becchi, Manish Parashar, Dorian C. Arnold

AUTHOR(S):Yusuke Nagasaka, Akira Nukada, Satoshi Matsuoka

ROOM:Level 4 - Lobby

ABSTRACT:

Many scientific and industrial simulations require solving large linear equations, whose bottleneck is sparse matrix vector multiplication (SpMV). Although some previous work has shown improvement of SpMV performance on GPU, the critical bottlenecks such as requirement of high memory bandwidth and low cache hit ratio due to random memory access to input vector still remain. We propose the state of the art sparse matrix format reducing memory access for GPU. Adaptive Multi-level Blocking (AMB) format compresses the column index by using 16-bit integer and several blocking optimizations, and we also devise effective SpMV kernel. We evaluated the performance of our approach for 62 positive definite large size matrices in single precision. AMB format achieves significant speedup of x2.83 on maximum and x1.75 on average compared to cuSparse library and x1.38 on maximum and x1.08 on average compared to yaSpMV, which is recently proposed fast SpMV library.

Chair/Author Details:

Michela Becchi, Manish Parashar, Dorian C. Arnold (Chair) - University of Missouri|Rutgers University|University of New Mexico|

Yusuke Nagasaka - Tokyo Institute of Technology

Akira Nukada - Tokyo Institute of Technology

Satoshi Matsuoka - Tokyo Institute of Technology

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