원문정보
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
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