Optimizing and scaling HPCG on tianhe-2: Early experience

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	Optimizing and scaling HPCG on tianhe-2: Early experience
	Zhang, Xianyi (1); Yang, Chao (1); Liu, Fangfang (1); Liu, Yiqun (1); Lu, Yutong (4)
	2014
会议名称	14th International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2014
页码	28-41
会议日期	August 24, 2014 - August 27, 2014
会议地点	Dalian, China
收录类别	EI
出版地	Springer Verlag
ISSN	3029743
ISBN	9783319111964
部门归属	(1) Institute of Software, Chinese Academy of Sciences, Beijing 100190, China; (2) State Key Laboratory of Computer Science, Chinese Academy of Sciences, Beijing 100190, China; (3) University of Chinese Academy of Sciences, Beijing 100049, China; (4) National University of Defense Technology, Changsha Hunan 410073, China
摘要	In this paper, a first attempt has been made on optimizing and scaling HPCG on the world's largest supercomputer, Tianhe-2. This early work focuses on the optimization of the CPU code without using the Intel Xeon Phi coprocessors. In our work, we reformulate the basic CG algorithm to minimize the cost of collective communication and employ several optimizing techniques such as SIMDization, loop unrolling, forward and backward sweep fusion, OpenMP parallization to further enhance the performance of kernels such as the sparse matrix vector multiplication, the symmetric Gauss-Seidel relaxation and the geometric multigrid v-cycle. We successfully scale the HPCG code from 256 up to 6,144 nodes (147,456 CPU cores) on Tianhe-2, with a nearly ideal weak scalability and an aggregate performance of 79.83 Tflops, which is 6.38X higher than the reference implementation. © 2014 Springer International Publishing Switzerland.; In this paper, a first attempt has been made on optimizing and scaling HPCG on the world's largest supercomputer, Tianhe-2. This early work focuses on the optimization of the CPU code without using the Intel Xeon Phi coprocessors. In our work, we reformulate the basic CG algorithm to minimize the cost of collective communication and employ several optimizing techniques such as SIMDization, loop unrolling, forward and backward sweep fusion, OpenMP parallization to further enhance the performance of kernels such as the sparse matrix vector multiplication, the symmetric Gauss-Seidel relaxation and the geometric multigrid v-cycle. We successfully scale the HPCG code from 256 up to 6,144 nodes (147,456 CPU cores) on Tianhe-2, with a nearly ideal weak scalability and an aggregate performance of 79.83 Tflops, which is 6.38X higher than the reference implementation. © 2014 Springer International Publishing Switzerland.
语种	英语
内容类型	会议论文
URI标识	http://ir.iscas.ac.cn/handle/311060/16618
专题	中国科学院软件研究所
推荐引用方式 GB/T 7714	Zhang, Xianyi ,Yang, Chao ,Liu, Fangfang ,et al. Optimizing and scaling HPCG on tianhe-2: Early experience[C]. Springer Verlag,2014:28-41.