中圖分類號: TP391 文獻標識碼: A DOI:10.16157/j.issn.0258-7998.212422 中文引用格式: 楊斌,,王敬宇,,劉衛(wèi)國,等. GRAPES區(qū)域模式的輸入輸出分析和優(yōu)化[J].電子技術應用,,2022,,48(1):39-45,52. 英文引用格式: Yang Bin,,Wang Jingyu,,Liu Weiguo,et al. Input/Output analysis and optimization for GRAPES regional model[J]. Application of Electronic Technique,,2022,,48(1):39-45,52.
Input/Output analysis and optimization for GRAPES regional model
Yang Bin1,,2,,Wang Jingyu3,Liu Weiguo1,,2,,Cai Huiyi2,Yu Fei4,,5,,Deng Liantang4,5,,Huang Liping4,,5
1.School of Software,Shandong University,Jinan 250101,,China,;2.National Supercomputing Center in Wuxi,Wuxi 214072,,China,; 3.National Research Center of Parallel Computer Engineering & Technology,Beijing 100086,,China,; 4.Numerical Weather Prediction Center of CMA,Beijing 100081,,China,;5.State Key Laboratory of Severe Weather,Beijing 100081,,China
Abstract: The new generation Global/Regional Assimilation and PreEdiction System(GRAPES) is a homegrown numerical weather prediction software developed by China Meteorological Administration(CMA). As the requirements for model resolution and prediction timeliness increase, the Input/Output(I/O) performance of GRAPES becomes a critical performance bottleneck. This paper performs a deep analysis of I/O behavior for the GRAPES regional model,,and proposes, designs and implements a high-performance I/O framework. This framework achieves a flexible and configurable output method through binary encoding and multiple I/O channels. At the same time, asynchronous I/O is included by non-blocking communication, which hides the I/O and communication overhead. The framework has been tested on the Sugon Pai supercomputer, and the results show that the framework can not only improve I/O performance by up to over ten times but also reduce the performance uncertainty caused by performance jitter.
Key words : I/O optimization;asynchronous I/O,;GRAPES,;CMA-MESO;regional model
0 引言
天氣預報與人民財產(chǎn)和生命安全休戚相關,。近年來高影響天氣事件(例如鄭州暴雨)的發(fā)生頻度和強度持續(xù)增加,,嚴重影響經(jīng)濟社會發(fā)展,威脅人民生命財產(chǎn)安全,。對高影響天氣的精準預報已經(jīng)成為國際熱點,,以及國家防災減災決策中的關鍵和迫切需求。自20世紀80年代起,,數(shù)值預報已成為國際天氣預報的主流發(fā)展趨勢,,天氣形勢預報時效目前達到甚至超過7天,而制作更精細的數(shù)值預報,,提高天氣預報準確率的關鍵是進一步提高數(shù)值預報的精確度[1],。超高分辨率模擬和高頻觀測資料的快速應用是當前提升數(shù)值預報精確度的關鍵,也是極其重要又極具挑戰(zhàn)的計算應用,。全球/區(qū)域多尺度統(tǒng)一的同化與數(shù)值預報系統(tǒng)(Global/Regional Assimilation and PreEdiction System,,GRAPES)[2]應運而生,GRAPES是我國歷時超過二十年自主研發(fā),,以多尺度通用非精力動力框架為核心開發(fā)建立的可插拔式的新一代通用數(shù)值分析同化與預報系統(tǒng),。目前GRAPES已成為我國中期數(shù)值天氣預報的業(yè)務化模式,不僅能夠預報大尺度形勢場,,而且在要素預報(如降水量,、風,、濕、壓等)準確性也在逐步提高,,逐漸在預報業(yè)務一線發(fā)揮著越來越大的作用[3-5],。