Development of error resilience technology for complex moment-based parallel eigensolvers
| Project/Area Number |
17K12690
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| Research Category |
Grant-in-Aid for Young Scientists (B)
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| Allocation Type | Multi-year Fund |
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| Research Field |
High performance computing
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| Research Institution | University of Tsukuba |
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Principal Investigator |
Imakura Akira 筑波大学, システム情報系, 准教授 (60610045)
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| Project Period (FY) |
2017-04-01 – 2022-03-31
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| Project Status |
Completed (Fiscal Year 2021)
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| Budget Amount *help |
¥4,030,000 (Direct Cost: ¥3,100,000、Indirect Cost: ¥930,000)
Fiscal Year 2020: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000)
Fiscal Year 2019: ¥1,040,000 (Direct Cost: ¥800,000、Indirect Cost: ¥240,000)
Fiscal Year 2018: ¥1,040,000 (Direct Cost: ¥800,000、Indirect Cost: ¥240,000)
Fiscal Year 2017: ¥780,000 (Direct Cost: ¥600,000、Indirect Cost: ¥180,000)
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| Keywords | 数値解析 / 高性能計算 / 耐障害技術 |
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| Outline of Final Research Achievements |
In this research project, we focused on the complex moment-based eigensolvers, which are massively parallel eigenvalue analysis methods originated in Japan and have been actively studied in recent years. We developed mathematical fault-tolerance techniques through theoretical error analysis of the method, aiming to realize fault-tolerance at the algorithm level. Theoretical and experimental evaluations have shown that the developed technology can obtain eigenpairs with high speed and accuracy even in the presence of failures. In addition, we have also developed technologies to improve usability for practical use. The results of this research project were presented at domestic and international conferences and submitted to international journals.
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| Academic Significance and Societal Importance of the Research Achievements |
近年のスーパーコンピュータの大規模・高性能化に伴い、システムの故障率が上昇しており、特に次世代スーパーコンピュータでの長時間シミュレーションの困難さが懸念されている。このため、システムレベルでの対策とともに、システム障害によらず正しい計算結果を与える「アルゴリズムレベルでの耐障害性」の実現が近年重要視されている。各種のシミュレーションで基盤となる固有値解析において、アルゴリズムレベルでの耐障害性を実現する本研究課題の成果は、次世代スーパーコンピュータ上での長時間シミュレーションの実現に寄与し、幅広い応用分野において、シミュレーションの大規模化・高精度化につながることが期待される。
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Report
(6 results)
Research Products
(83 results)
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[Journal Article] ESSEX: Equipping Sparse Solvers For Exascale,2020
Author(s)
Christie L. Alappat, Andreas Alvermann, Achim Basermann, Holger Fehske, Yasunori Futamura, Martin Galgon, Georg Hager, Sarah Huber, Akira Imakura, Masatoshi Kawai, Moritz Kreutzer, Bruno Lang, Kengo Nakajima, Melven Rohrig-Zollner, Tetsuya Sakurai, Faisal Shahzad, Jonas Thies, Gerhard Wellein,
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Journal Title
In: Bungartz HJ., Reiz S., Uekermann B., Neumann P., Nagel W. (eds), Software for Exascale Computing - SPPEXA 2016-2019, Lecture Notes in Computational Science and Engineering
Volume: 136
Pages: 143-187
DOI
ISBN
9783030479558, 9783030479565
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Peer Reviewed / Open Access / Int'l Joint Research
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[Book] 数値線形代数の数理とHPC2018
Author(s)
山本 有作, 多田野 寛人, 今倉 暁, 相島 健助, 宮田 考史, 櫻井 鉄也, 中村 佳正, 保國 惠一, 曽我部 知広, 中島 研吾, 深谷 猛, 二村 保徳, 大島 聡史
Total Pages
336
Publisher
共立出版
ISBN
9784320019553
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