Earticle

초록

영어

GPU is the suitable equipment for accelerating computing-intensive applications in order to get the higher throughput for High Performance Computing (HPC). Sparse Matrix-Vector Multiplication (SpMV) is the core algorithm of HPC, so the SpMV’s throughput on GPU may affect the throughput on HPC platform. In the paper, we focus on the latency of reduction routine in SpMV included in CUSP, such as accessing shared memory and bank conflicting while multiple threads simultaneously accessing the same bank. We provide shuffle method to reduce the partial results instead of reducing in the shared memory in order to improve the throughput of SpMV on Kepler GPU. Experiments show that shuffle method can improve the throughput up to 9% of the original routine of SpMV in CUSP on average.

목차

Abstract
 1. Introduction
 2. Preliminaries
  2.1. General Purpose Computing with GPU
  2.2. Compressed Sparse Row
  2.3. Shared Memory Reducing Based SpMV
 3. Shuffle Reduction Based CSR’s SpMV on GPU
 4. Experimental Results and Discussion
  4.1. Experimental Setup
  4.2. Experimental Results and Discussion
 5. Conclusion
 References

키워드

저자

• Yuan Tao [ College of Mathematics, Jilin Normal University, Siping Jilin, China ]
• Huang Zhi-Bin [ Beijing Key Lab of Intelligent Telecommunication Software and Multimedia,Beijing University of Posts and Telecommunications, Beijing China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Grid and Distributed Computing

• 간기

  격월간

• pISSN

  2005-4262

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605