Acceleration of Data Assimilation for Rainfall Prediction with Co-design of Hardware and Software

• Item type: SIG Technical Reports(1)
• 公開日: 2024-08-01
• タイトル: Acceleration of Data Assimilation for Rainfall Prediction with Co-design of Hardware and Software
• タイトル / 言語: en
• タイトル: Acceleration of Data Assimilation for Rainfall Prediction with Co-design of Hardware and Software
• 言語: eng
• キーワード / 主題Scheme: Other
• キーワード / 主題: ビックデータ処理・信頼性
• 資源タイプ / 資源タイプ識別子: http://purl.org/coar/resource_type/c_18gh
• 資源タイプ: technical report
• 著者所属: Fujitsu Ltd.
• 著者所属: RIKEN
• 著者所属: Fujitsu Ltd
• 著者所属: Fujitsu Ltd
• 著者所属(英): en
• 著者所属(英): Fujitsu Ltd.
• 著者所属(英): en
• 著者所属(英): RIKEN
• 著者所属(英): en
• 著者所属(英): Fujitsu Ltd
• 著者所属(英): en
• 著者所属(英): Fujitsu Ltd
• 著者名: Sameer, Deshmukh; Arata, Amemiya; Takumi, Honda; Elmor, Lang Ian
• 著者名(英): Sameer, Deshmukh; Arata, Amemiya; Takumi, Honda; Elmor, Lang Ian
• 論文抄録 / 内容記述タイプ: Other
• 論文抄録 / 内容記述: Rainfall forecasting is an important application of scientific computing that has a far-reaching impact on humanity. Accurate and timely forecasts can make the difference between life and death for people affected by severe weather conditions. High quality forecasts depend mainly upon availability of data and computational resources. The MP-PAWR at Saitama University generates high quality distributions of 3D radar reflectivity at 30 second intervals. The data from this radar has been used to generate rainfall density forecasts using the SCALE-LETKF algorithm. The SCALE-LETKF algorithm is very inefficient on currently available architectures, utilizing only about 5% of the available computational capacity of Fugaku. Improvement in the computational efficiency of SCALE-LETKF can hold tremendous value for the accuracy, speed and energy efficiency of rainfall forecasts. LETKF from SCALE-LETKF occupies a majority of the computational resources. LETKF is designed for processing large amounts of data, and has been written over several years, comprising more than 20,000 lines of FORTRAN code. We propose a mini-application that imitates the most important performance and numerical characteristics of LETKF. We then propose an alternative hardware accelerator that can potentially achieve higher computational efficiency than currently available hardware.
• 論文抄録(英) / 内容記述タイプ: Other
• 論文抄録(英) / 内容記述: Rainfall forecasting is an important application of scientific computing that has a far-reaching impact on humanity. Accurate and timely forecasts can make the difference between life and death for people affected by severe weather conditions. High quality forecasts depend mainly upon availability of data and computational resources. The MP-PAWR at Saitama University generates high quality distributions of 3D radar reflectivity at 30 second intervals. The data from this radar has been used to generate rainfall density forecasts using the SCALE-LETKF algorithm. The SCALE-LETKF algorithm is very inefficient on currently available architectures, utilizing only about 5% of the available computational capacity of Fugaku. Improvement in the computational efficiency of SCALE-LETKF can hold tremendous value for the accuracy, speed and energy efficiency of rainfall forecasts. LETKF from SCALE-LETKF occupies a majority of the computational resources. LETKF is designed for processing large amounts of data, and has been written over several years, comprising more than 20,000 lines of FORTRAN code. We propose a mini-application that imitates the most important performance and numerical characteristics of LETKF. We then propose an alternative hardware accelerator that can potentially achieve higher computational efficiency than currently available hardware.
• 書誌レコードID / 収録物識別子タイプ: NCID
• 書誌レコードID / 収録物識別子: AN10096105
• 書誌情報: 研究報告システム・アーキテクチャ（ARC）; 巻 2024-ARC-258, 号 24, p. 1-6, 発行日 2024-08-01
• ISSN / 収録物識別子タイプ: ISSN
• ISSN / 収録物識別子: 2188-8574
• Notice: SIG Technical Reports are nonrefereed and hence may later appear in any journals, conferences, symposia, etc.
• 出版者 / 言語: ja
• 出版者: 情報処理学会