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231226s2023 xx |||||o 00| ||eng c |
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|a 10.1002/jcc.27075
|2 doi
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|a pubmed24n1171.xml
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|a (DE-627)NLM351591257
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|a DE-627
|b ger
|c DE-627
|e rakwb
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|a eng
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| 100 |
1 |
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|a Ma, Yingjin
|e verfasserin
|4 aut
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| 245 |
1 |
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|a Machine-learning assisted scheduling optimization and its application in quantum chemical calculations
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|c 2023
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|a Text
|b txt
|2 rdacontent
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|a ƒaComputermedien
|b c
|2 rdamedia
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|a ƒa Online-Ressource
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|2 rdacarrier
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|a Date Completed 24.03.2023
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|a Date Revised 24.03.2023
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|a published: Print-Electronic
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|a Citation Status PubMed-not-MEDLINE
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|a © 2023 Wiley Periodicals LLC.
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|a Easy and effective usage of computational resources is crucial for scientific calculations, both from the perspectives of timeliness and economic efficiency. This work proposes a bi-level optimization framework to optimize the computational sequences. Machine-learning (ML) assisted static load-balancing, and different dynamic load-balancing algorithms can be integrated. Consequently, the computational and scheduling engine of the ParaEngine is developed to invoke optimized quantum chemical (QC) calculations. Illustrated benchmark calculations include high-throughput drug suit, solvent model, P38 protein, and SARS-CoV-2 systems. The results show that the usage rate of given computational resources for high throughput and large-scale fragmentation QC calculations can primarily profit, and faster accomplishing computational tasks can be expected when employing high-performance computing (HPC) clusters
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|a Journal Article
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| 650 |
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|a distributed computing
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| 650 |
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|a fragmentation approach
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| 650 |
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|a high throughput computing
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| 650 |
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4 |
|a interaction energy calculations
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| 650 |
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4 |
|a load-balancing
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| 700 |
1 |
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|a Li, ZhiYing
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Chen, Xin
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Ding, Bowen
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Li, Ning
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Lu, Teng
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Zhang, Baohua
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Suo, BingBing
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Jin, Zhong
|e verfasserin
|4 aut
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| 773 |
0 |
8 |
|i Enthalten in
|t Journal of computational chemistry
|d 1984
|g 44(2023), 12 vom: 05. Mai, Seite 1174-1188
|w (DE-627)NLM098138448
|x 1096-987X
|7 nnns
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| 773 |
1 |
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|g volume:44
|g year:2023
|g number:12
|g day:05
|g month:05
|g pages:1174-1188
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|u http://dx.doi.org/10.1002/jcc.27075
|3 Volltext
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