| Title: | a peta-scalable cpu-gpu algorithm for global atmospheric simulations |
| Author: | Yang Chao
; Xue Wei
; Fu Haohuan
; Gan Lin
; Li Linfeng
; Xu Yangtong
; Lu Yutong
; Sun Jiachang
; Yang Guangwen
; Zheng Weimin
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| Source: | Proceedings of the ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, PPOPP
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| Conference Name: | 18th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, PPoPP 2013
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| Conference Date: | February 23, 2013 - February 27, 2013
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| Issued Date: | 2013
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| Conference Place: | Shenzhen, China
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| Keyword: | Communication
; Computer architecture
; Computer programming languages
; Hybrid systems
; Mathematical models
; Multitasking
; Parallel algorithms
; Parallel programming
; Program processors
; Scalability
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| Indexed Type: | EI
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| ISBN: | 9781450319225
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| Department: | (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
|
| Sponsorship: | ACM SIGPLAN
|
| Abstract: | 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. |
| English Abstract: | 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. |
| Language: | 英语
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| Citation statistics: |
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| Content Type: | 会议论文
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| URI: | http://ir.iscas.ac.cn/handle/311060/15974
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| Appears in Collections: | 软件所图书馆_会议论文
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| Recommended Citation: | |
Yang Chao,Xue Wei,Fu Haohuan,et al. a peta-scalable cpu-gpu algorithm for global atmospheric simulations[C]. 见:18th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, PPoPP 2013. Shenzhen, China. February 23, 2013 - February 27, 2013.
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