中国科学院软件研究所机构知识库
Advanced  
ISCAS OpenIR  > 软件所图书馆  > 会议论文
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
Source: Proceedings of the ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, PPOPP
Conference Name: 18th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, PPoPP 2013
Conference Date: February 23, 2013 - February 27, 2013
Issued Date: 2013
Conference Place: Shenzhen, China
Keyword: Communication ; Computer architecture ; Computer programming languages ; Hybrid systems ; Mathematical models ; Multitasking ; Parallel algorithms ; Parallel programming ; Program processors ; Scalability
Indexed Type: EI
ISBN: 9781450319225
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
Citation statistics:
Content Type: 会议论文
URI: http://ir.iscas.ac.cn/handle/311060/15974
Appears in Collections:软件所图书馆_会议论文

Files in This Item:

There are no files associated with this item.


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.
Service
Recommend this item
Sava as my favorate item
Show this item's statistics
Export Endnote File
Google Scholar
Similar articles in Google Scholar
[Yang Chao]'s Articles
[Xue Wei]'s Articles
[Fu Haohuan]'s Articles
CSDL cross search
Similar articles in CSDL Cross Search
[Yang Chao]‘s Articles
[Xue Wei]‘s Articles
[Fu Haohuan]‘s Articles
Related Copyright Policies
Null
Social Bookmarking
Add to CiteULike Add to Connotea Add to Del.icio.us Add to Digg Add to Reddit
所有评论 (0)
暂无评论
 
评注功能仅针对注册用户开放,请您登录
您对该条目有什么异议,请填写以下表单,管理员会尽快联系您。
内 容:
Email:  *
单位:
验证码:   刷新
您在IR的使用过程中有什么好的想法或者建议可以反馈给我们。
标 题:
 *
内 容:
Email:  *
验证码:   刷新

Items in IR are protected by copyright, with all rights reserved, unless otherwise indicated.

 

 

Valid XHTML 1.0!
Copyright © 2007-2019  中国科学院软件研究所 - Feedback
Powered by CSpace