WEKO3
アイテム
Scalable Empirical Dynamic Modeling With Parallel Computing and Approximate k-NN Search
http://hdl.handle.net/10061/0002000596
http://hdl.handle.net/10061/0002000596f21e0819-665b-48df-a5d4-d6f320ebaed4
| アイテムタイプ | 学術雑誌論文 / Journal Article(1) | |||||||||||
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| 公開日 | 2024-10-18 | |||||||||||
| タイトル | ||||||||||||
| タイトル | Scalable Empirical Dynamic Modeling With Parallel Computing and Approximate k-NN Search | |||||||||||
| 言語 | ||||||||||||
| 言語 | eng | |||||||||||
| キーワード | ||||||||||||
| 主題Scheme | Other | |||||||||||
| 主題 | Empirical dynamic modeling | |||||||||||
| キーワード | ||||||||||||
| 主題Scheme | Other | |||||||||||
| 主題 | high-performance computing | |||||||||||
| キーワード | ||||||||||||
| 主題Scheme | Other | |||||||||||
| 主題 | time-series analysis | |||||||||||
| キーワード | ||||||||||||
| 主題Scheme | Other | |||||||||||
| 主題 | performance portability | |||||||||||
| キーワード | ||||||||||||
| 主題Scheme | Other | |||||||||||
| 主題 | high-performance data analytics | |||||||||||
| 資源タイプ | ||||||||||||
| 資源タイプ | journal article | |||||||||||
| アクセス権 | ||||||||||||
| アクセス権 | open access | |||||||||||
| 著者 |
Takahashi, Keichi
× Takahashi, Keichi
× 市川, 昊平× Park, Joseph
× Pao, Gerald M.
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| 抄録 | ||||||||||||
| 内容記述タイプ | Abstract | |||||||||||
| 内容記述 | Empirical Dynamic Modeling (EDM) is a mathematical framework for modeling and predicting non-linear time series data. Although EDM is increasingly adopted in various research fields, its application to large-scale data has been limited due to its high computational cost. This article presents kEDM, a high-performance implementation of EDM for analyzing large-scale time series datasets. kEDM adopts the Kokkos performance-portable programming model to efficiently run on both CPU and GPU while sharing a single code base. We also conduct hardware-specific optimization of performance-critical kernels. kEDM achieved up to 6.58× speedup in pairwise causal inference of real-world biology datasets compared to an existing EDM implementation. Furthermore, we integrate multiple approximate k-NN search algorithms into EDM to enable the analysis of extremely large datasets that were intractable with conventional EDM based on exhaustive k-NN search. EDM-based time series forecast enhanced with approximate k-NN search demonstrated up to 790× speedup compared to conventional Simplex projection with less than 1% increase in MAPE. | |||||||||||
| 書誌情報 |
en : IEEE Access 巻 11, p. 68171-68183, 発行日 2023-06-27 |
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| 出版者 | ||||||||||||
| 出版者 | Institute of Electrical and Electronics Engineers | |||||||||||
| ISSN | ||||||||||||
| 収録物識別子タイプ | EISSN | |||||||||||
| 収録物識別子 | 2169-3536 | |||||||||||
| 出版者版DOI | ||||||||||||
| 関連タイプ | isReplacedBy | |||||||||||
| 識別子タイプ | DOI | |||||||||||
| 関連識別子 | https://doi.org/10.1109/ACCESS.2023.3289836 | |||||||||||
| 出版者版URI | ||||||||||||
| 関連タイプ | isReplacedBy | |||||||||||
| 識別子タイプ | URI | |||||||||||
| 関連識別子 | https://ieeexplore.ieee.org/document/10164090 | |||||||||||
| 権利 | ||||||||||||
| 権利情報Resource | https://creativecommons.org/licenses/by-nc-nd/4.0/ | |||||||||||
| 権利情報 | This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. For more information, see https://creativecommons.org/licenses/by-nc-nd/4.0/ | |||||||||||
| 著者版フラグ | ||||||||||||
| 出版タイプ | NA | |||||||||||
