Enabling and scaling a global shallow-water atmospheric model on Tianhe-2

ISCAS OpenIR

	Enabling and scaling a global shallow-water atmospheric model on Tianhe-2
	Xue, Wei (1); Yang, Chao (2); Fu, Haohuan (3); Wang, Xinliang (1); Xu, Yangtong (1); Gan, Lin (1); Lu, Yutong (5); Zhu, Xiaoqian (5)
	2014
会议名称	28th IEEE International Parallel and Distributed Processing Symposium, IPDPS 2014
页码	745-754
会议日期	May 19, 2014 - May 23, 2014
会议地点	Phoenix, AZ, United states
收录类别	EI
出版地	IEEE Computer Society
ISSN	15302075
ISBN	9780769552071
部门归属	(1) Dept. of Computer Science and Technology, Tsinghua University, Beijing 100084, China; (2) Institute of Software, Chinese Academy of Sciences, Beijing 100190, China; (3) Ministry of Education Key Laboratory for Earth System Modeling, And Center for Earth System Science, Tsinghua University, Beijing 100084, China; (4) State Key Laboratory of Computer Science, Chinese Academy of Sciences, Beijing 100190, China; (5) Dept. of Computer Science and Technology, National University of Defense Technology, Changsha, Hunan 410073, China
摘要	This paper presents a hybrid algorithm for the petascale global simulation of atmospheric dynamics on Tianhe-2, the world's current top-ranked supercomputer developed by China's National University of Defense Technology (NUDT). Tianhe-2 is equipped with both Intel Xeon CPUs and Intel Xeon Phi accelerators. A key idea of the hybrid algorithm is to enable flexible domain partition between an arbitrary number of processors and accelerators, so as to achieve a balanced and efficient utilization of the entire system. We also present an asynchronous and concurrent data transfer scheme to reduce the communication overhead between CPU and accelerators. The acceleration of our global atmospheric model is conducted to improve the use of the Intel MIC architecture. For the single-node test on Tianhe-2 against two Intel Ivy Bridge CPUs (24 cores), we can achieve 2.07x, 3.18x, and 4.35x speedups when using one, two, and three Intel Xeon Phi accelerators respectively. The average performance gain from SIMD vectorization on the Intel Xeon Phi processors is around 5x (out of the 8x theoretical case). Based on successful computation-communication overlapping, large-scale tests indicate that a nearly ideal weak-scaling efficiency of 93.5% is obtained when we gradually increase the number of nodes from 6 to 8,664 (nearly 1.7 million cores). In the strong-scaling test, the parallel efficiency is about 77% when the number of nodes increases from 1,536 to 8,664 for a fixed 65,664 × 5,664 × 6 mesh with 77.6 billion unknowns. © 2014 IEEE.; This paper presents a hybrid algorithm for the petascale global simulation of atmospheric dynamics on Tianhe-2, the world's current top-ranked supercomputer developed by China's National University of Defense Technology (NUDT). Tianhe-2 is equipped with both Intel Xeon CPUs and Intel Xeon Phi accelerators. A key idea of the hybrid algorithm is to enable flexible domain partition between an arbitrary number of processors and accelerators, so as to achieve a balanced and efficient utilization of the entire system. We also present an asynchronous and concurrent data transfer scheme to reduce the communication overhead between CPU and accelerators. The acceleration of our global atmospheric model is conducted to improve the use of the Intel MIC architecture. For the single-node test on Tianhe-2 against two Intel Ivy Bridge CPUs (24 cores), we can achieve 2.07x, 3.18x, and 4.35x speedups when using one, two, and three Intel Xeon Phi accelerators respectively. The average performance gain from SIMD vectorization on the Intel Xeon Phi processors is around 5x (out of the 8x theoretical case). Based on successful computation-communication overlapping, large-scale tests indicate that a nearly ideal weak-scaling efficiency of 93.5% is obtained when we gradually increase the number of nodes from 6 to 8,664 (nearly 1.7 million cores). In the strong-scaling test, the parallel efficiency is about 77% when the number of nodes increases from 1,536 to 8,664 for a fixed 65,664 × 5,664 × 6 mesh with 77.6 billion unknowns. © 2014 IEEE.
语种	英语
内容类型	会议论文
URI标识	http://ir.iscas.ac.cn/handle/311060/16636
专题	中国科学院软件研究所
推荐引用方式 GB/T 7714	Xue, Wei ,Yang, Chao ,Fu, Haohuan ,et al. Enabling and scaling a global shallow-water atmospheric model on Tianhe-2[C]. IEEE Computer Society,2014:745-754.