optimizing spmv for diagonal sparse matrices on gpu
文献类型：会议论文
| 作者 | Sun Xiangzheng ; Zhang Yunquan ; Wang Ting ; Zhang Xianyi ; Yuan Liang ; Rao Li |
| 出版日期 | 2011 |
| 会议名称 | 40th International Conference on Parallel Processing, ICPP 2011 |
| 会议日期 | September |
| 会议地点 | Taipei City, Taiwan |
| 关键词 | Memory architecture Network components Optimization Program processors |
| 页码 | 492-501 |
| 中文摘要 | Sparse Matrix-Vector multiplication (SpMV) is an important computational kernel in scientific applications. Its performance highly depends on the nonzero distribution of sparse matrices. In this paper, we propose a new storage format for diagonal sparse matrices, defined as Compressed Row Segment with Diagonal-pattern (CRSD). In CRSD, we design diagonal patterns to represent the diagonal distribution. As the Graphics Processing Units (GPUs) have tremendous computation power and OpenCL makes them more suitable for the scientific computing, we implement the SpMV for CRSD format on the GPUs using OpenCL. Since the OpenCL kernels are complied at runtime, we design the code generator to produce the codelets for all diagonal patterns after storing matrices into CRSD format. Specifically, the generated codelets already contain the index information of nonzeros, which reduces the memory pressure during the SpMV operation. Furthermore, the code generator also utilizes property of memory architecture and thread schedule on the GPUs to improve the performance. In the evaluation, we select four storage formats from prior state-of-the-art implementations (Bell and Garland, 2009) on GPU. Experimental results demonstrate that the speedups reach up to 1.52 and 1.94 in comparison with the optimal implementation of the four formats for the double and single precision respectively. We also evaluate on a two-socket quad-core Intel Xeon system. The speedups reach up to 11.93 and 12.79 in comparison with CSR format under 8 threads for the double and single precision respectively. © 2011 IEEE. |
| 英文摘要 | Sparse Matrix-Vector multiplication (SpMV) is an important computational kernel in scientific applications. Its performance highly depends on the nonzero distribution of sparse matrices. In this paper, we propose a new storage format for diagonal sparse matrices, defined as Compressed Row Segment with Diagonal-pattern (CRSD). In CRSD, we design diagonal patterns to represent the diagonal distribution. As the Graphics Processing Units (GPUs) have tremendous computation power and OpenCL makes them more suitable for the scientific computing, we implement the SpMV for CRSD format on the GPUs using OpenCL. Since the OpenCL kernels are complied at runtime, we design the code generator to produce the codelets for all diagonal patterns after storing matrices into CRSD format. Specifically, the generated codelets already contain the index information of nonzeros, which reduces the memory pressure during the SpMV operation. Furthermore, the code generator also utilizes property of memory architecture and thread schedule on the GPUs to improve the performance. In the evaluation, we select four storage formats from prior state-of-the-art implementations (Bell and Garland, 2009) on GPU. Experimental results demonstrate that the speedups reach up to 1.52 and 1.94 in comparison with the optimal implementation of the four formats for the double and single precision respectively. We also evaluate on a two-socket quad-core Intel Xeon system. The speedups reach up to 11.93 and 12.79 in comparison with CSR format under 8 threads for the double and single precision respectively. © 2011 IEEE. |
| 收录类别 | EI |
| 会议主办者 | Int. Assoc. Comput. Commun. (IACC) |
| 会议录 | Proceedings of the International Conference on Parallel Processing
 |
| 语种 | 英语 |
| ISSN号 | 0190-3918 |
| ISBN号 | 9780769545103 |
| 源URL | [http://ir.iscas.ac.cn/handle/311060/16207]  |
| 专题 | 软件研究所_软件所图书馆_会议论文 |
| 推荐引用方式 GB/T 7714 | Sun Xiangzheng,Zhang Yunquan,Wang Ting,et al. optimizing spmv for diagonal sparse matrices on gpu[C]. 见:40th International Conference on Parallel Processing, ICPP 2011. Taipei City, Taiwan. September. |
入库方式： OAI收割
来源：软件研究所
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