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Efficient Embarrassingly Parallel on Graphics Processor Unit

Chunye Gong, Jie Liu, Jin Qin, Qingfeng Hu, Zhenghu Gong
Dept. of Comput. Sci., Nat. Univ. of Defense Technol., Changsha, China
2nd International Conference on Education Technology and Computer (ICETC), 2010

@inproceedings{gong2010efficient,

   title={Efficient Embarrassingly Parallel on Graphics Processor Unit},

   author={Gong, C. and Liu, J. and Qin, J. and Hu, Q. and Gong, Z.},

   booktitle={Education Technology and Computer (ICETC), 2010 2nd International Conference on},

   volume={4},

   pages={V4–400},

   organization={IEEE},

   year={2010}

}

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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.
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