| Item type |
SIG Technical Reports(1) |
| 公開日 |
2020-07-20 |
| タイトル |
|
|
タイトル |
High-performance cloud computing for exhaustive protein-protein docking |
| タイトル |
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|
言語 |
en |
|
タイトル |
High-performance cloud computing for exhaustive protein-protein docking |
| 言語 |
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|
言語 |
eng |
| 資源タイプ |
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|
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 |
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Department of Computer Science, School of Computing, Tokyo Institute of Technology／Ahead Biocomputing, Co. Ltd. |
| 著者所属 |
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Department of Computer Science, School of Computing, Tokyo Institute of Technology／RWBC-OIL, AIST |
| 著者所属 |
|
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Department of Computer Science, School of Computing, Tokyo Institute of Technology／Ahead Biocomputing, Co. Ltd. |
| 著者所属(英) |
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|
en |
|
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Department of Computer Science, School of Computing, Tokyo Institute of Technology / Ahead Biocomputing, Co. Ltd. |
| 著者所属(英) |
|
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|
en |
|
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Department of Computer Science, School of Computing, Tokyo Institute of Technology / RWBC-OIL, AIST |
| 著者所属(英) |
|
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|
en |
|
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Department of Computer Science, School of Computing, Tokyo Institute of Technology / Ahead Biocomputing, Co. Ltd. |
| 著者名 |
Masahito, Ohue
Kento, Aoyama
Yutaka, Akiyama
|
| 著者名(英) |
Masahito, Ohue
Kento, Aoyama
Yutaka, Akiyama
|
| 論文抄録 |
|
|
内容記述タイプ |
Other |
|
内容記述 |
Public cloud computing environments have achieved remarkable improvements in computational performance in recent years, and are also expected to be able to perform massively parallel computing. As the cloud enables users to use thousands of CPU cores and GPU accelerators casually, and various software types can be used very easily by cloud images, the cloud is beginning to be used in the field of bioinformatics. In this study, we ported the original protein-protein interaction prediction (protein-protein docking) software, MEGADOCK, into Microsoft Azure as an example of an HPC cloud environment. A cloud parallel computing environment with up to 1,600 CPU cores and 960 GPUs was constructed using four CPU instance types and two GPU instance types, and the parallel computing performance was evaluated. Our MEGADOCK on Azure system showed a strong scaling value of 0.93 for the CPU instance when H16 instance with 100 instances were used compared to 50, and a strong scaling value of 0.89 for the GPU instance when NC24 instance with 20 were used compared to 5. Moreover, the results of the usage fee and total computation time supported that using a GPU instance reduced the computation time of MEGADOCK and the cloud usage fee required for the computation. The developed environment deployed on the cloud is highly portable, making it suitable for applications in which an on-demand and large-scale HPC environment is desirable. |
| 論文抄録(英) |
|
|
内容記述タイプ |
Other |
|
内容記述 |
Public cloud computing environments have achieved remarkable improvements in computational performance in recent years, and are also expected to be able to perform massively parallel computing. As the cloud enables users to use thousands of CPU cores and GPU accelerators casually, and various software types can be used very easily by cloud images, the cloud is beginning to be used in the field of bioinformatics. In this study, we ported the original protein-protein interaction prediction (protein-protein docking) software, MEGADOCK, into Microsoft Azure as an example of an HPC cloud environment. A cloud parallel computing environment with up to 1,600 CPU cores and 960 GPUs was constructed using four CPU instance types and two GPU instance types, and the parallel computing performance was evaluated. Our MEGADOCK on Azure system showed a strong scaling value of 0.93 for the CPU instance when H16 instance with 100 instances were used compared to 50, and a strong scaling value of 0.89 for the GPU instance when NC24 instance with 20 were used compared to 5. Moreover, the results of the usage fee and total computation time supported that using a GPU instance reduced the computation time of MEGADOCK and the cloud usage fee required for the computation. The developed environment deployed on the cloud is highly portable, making it suitable for applications in which an on-demand and large-scale HPC environment is desirable. |
| 書誌レコードID |
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|
収録物識別子タイプ |
NCID |
|
収録物識別子 |
AN10505667 |
| 書誌情報 |
研究報告数理モデル化と問題解決（MPS）
巻 2020-MPS-129,
号 6,
p. 1-4,
発行日 2020-07-20
|
| ISSN |
|
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収録物識別子タイプ |
ISSN |
|
収録物識別子 |
2188-8833 |
| Notice |
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SIG Technical Reports are nonrefereed and hence may later appear in any journals, conferences, symposia, etc. |
| 出版者 |
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言語 |
ja |
|
出版者 |
情報処理学会 |
IPSJ-MPS20129006.pdf (825.4 kB)
