Efficient Embarrassingly Parallel on Graphics Processor Unit

摘要：

The Embarrassingly Parallel (EP) is one kernel benchmark of NAS Parallel Benchmarks (NPB) which are a set of programs designed to help evaluate the performance of parallel supercomputers. In the EP benchmark, two-dimensional statistics are accumulated from a large number of Gaussian pseudo-random numbers, which produced by Linear Congruential Generator (LCG). In this paper, we present the design and implementation of EP on the powerful Graphics Processor Unit Tesla T10 with CUDA. While keeping the main framework of NPB EP, comparative results show that the performance of our GPU-based implementation is up to 871.57 Mop/s. This is roughly 1.38 times faster than the throughput previously achieved on the same GPU and outperforms equivalent 4 cores CPU by about 11.33 times.

关键词： NPB Embarrassingly Parallel (EP) benchmark GPU CUDA

作者: Chunye Gong Jie Liu Jin Qin Qingfeng Hu Zhenghu Gong

作者单位: Department of Computer Sciences National University of Defense Technology Changsha, China

会议类型: 国际会议

会议名称: 2010 2nd International Conference on Education Technology and Computer(第二届IEEE教育技术与计算机国际会议 ICETC 2010)

会议地点: 上海

会议语种:英文

页码: 400-404

在线出版日期: 2010-06-22（万方平台首次上网日期，不代表论文的发表时间）