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],。
