Machine-learning assisted scheduling optimization and its application in quantum chemical calculations.
J Comput Chem
; 44(12): 1174-1188, 2023 May 05.
Article
in English
| MEDLINE | ID: covidwho-2232813
ABSTRACT
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.
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Language:
English
Journal:
J Comput Chem
Journal subject:
Chemistry
Year:
2023
Document Type:
Article
Affiliation country:
Jcc.27075
Similar
MEDLINE
...
LILACS
LIS