a peta-scalable cpu-gpu algorithm for global atmospheric simulations

ISCAS OpenIR

	a peta-scalable cpu-gpu algorithm for global atmospheric simulations
	Yang Chao; Xue Wei; Fu Haohuan; Gan Lin; Li Linfeng; Xu Yangtong; Lu Yutong; Sun Jiachang; Yang Guangwen; Zheng Weimin
	2013
会议名称	18th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, PPoPP 2013
会议录名称	Proceedings of the ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, PPOPP
页码	1-11
会议日期	February 23, 2013 - February 27, 2013
会议地点	Shenzhen, China
收录类别	EI
ISBN	9781450319225
部门归属	(1) Institute of Software Chinese Academy of Sciences Beijing China; (2) Department of Computer Science and Technology Tsinghua University Beijing China; (3) Ministry of Education Key Laboratory for Earth System Modeling Center for Earth System Science Tsinghua University Beijing China; (4) Department of Computer Science and Technology National University of Defense Technology Changsha Hunan China; (5) State Key Laboratory of Space Weather Chinese Academy of Sciences Beijing China
摘要	Developing highly scalable algorithms for global atmospheric modeling is becoming increasingly important as scientists inquire to understand behaviors of the global atmosphere at extreme scales. Nowadays, heterogeneous architecture based on both processors and accelerators is becoming an important solution for large-scale computing. However, large-scale simulation of the global atmosphere brings a severe challenge to the development of highly scalable algorithms that fit well into state-of-the-art heterogeneous systems. Although successes have been made on GPU-accelerated computing in some top-level applications, studies on fully exploiting heterogeneous architectures in global atmospheric modeling are still very less to be seen, due in large part to both the computational difficulties of the mathematical models and the requirement of high accuracy for long term simulations. In this paper, we propose a peta-scalable hybrid algorithm that is successfully applied in a cubed-sphere shallow-water model in global atmospheric simulations. We employ an adjustable partition between CPUs and GPUs to achieve a balanced utilization of the entire hybrid system, and present a pipe-flow scheme to conduct conflict-free inter-node communication on the cubed-sphere geometry and to maximize communication-computation overlap. Systematic optimizations for multithreading on both GPU and CPU sides are performed to enhance computing throughput and improve memory efficiency. Our experiments demonstrate nearly ideal strong and weak scalabilities on up to 3,750 nodes of the Tianhe-1A. The largest run sustains a performance of 0.8 Pflops in double precision (32% of the peak performance), using 45,000 CPU cores and 3,750 GPUs. © 2013 ACM.; Developing highly scalable algorithms for global atmospheric modeling is becoming increasingly important as scientists inquire to understand behaviors of the global atmosphere at extreme scales. Nowadays, heterogeneous architecture based on both processors and accelerators is becoming an important solution for large-scale computing. However, large-scale simulation of the global atmosphere brings a severe challenge to the development of highly scalable algorithms that fit well into state-of-the-art heterogeneous systems. Although successes have been made on GPU-accelerated computing in some top-level applications, studies on fully exploiting heterogeneous architectures in global atmospheric modeling are still very less to be seen, due in large part to both the computational difficulties of the mathematical models and the requirement of high accuracy for long term simulations. In this paper, we propose a peta-scalable hybrid algorithm that is successfully applied in a cubed-sphere shallow-water model in global atmospheric simulations. We employ an adjustable partition between CPUs and GPUs to achieve a balanced utilization of the entire hybrid system, and present a pipe-flow scheme to conduct conflict-free inter-node communication on the cubed-sphere geometry and to maximize communication-computation overlap. Systematic optimizations for multithreading on both GPU and CPU sides are performed to enhance computing throughput and improve memory efficiency. Our experiments demonstrate nearly ideal strong and weak scalabilities on up to 3,750 nodes of the Tianhe-1A. The largest run sustains a performance of 0.8 Pflops in double precision (32% of the peak performance), using 45,000 CPU cores and 3,750 GPUs. © 2013 ACM.
关键词	Communication Computer Architecture Computer Programming Languages Hybrid Systems Mathematical Models Multitasking Parallel Algorithms Parallel Programming Program Processors Scalability
主办者	ACM SIGPLAN
语种	英语
WOS记录号	WOS:000324158900001
引用统计
内容类型	会议论文
URI标识	http://ir.iscas.ac.cn/handle/311060/15974
专题	中国科学院软件研究所
推荐引用方式 GB/T 7714	Yang Chao,Xue Wei,Fu Haohuan,et al. a peta-scalable cpu-gpu algorithm for global atmospheric simulations[C],2013:1-11.