Extensible and Scalable Adaptive Sampling on Supercomputers

被引:12
|
作者
Hruska, Eugen [1 ,2 ]
Balasubramanian, Vivekanandan [3 ]
Lee, Hyungro [3 ]
Jha, Shantenu [3 ]
Clementi, Cecilia [1 ,2 ,4 ,5 ]
机构
[1] Rice Univ, Ctr Theoret Biol Phys, Houston, TX 77005 USA
[2] Rice Univ, Dept Phys & Astron, Houston, TX 77005 USA
[3] Rutgers State Univ, Dept Elect & Comp Engn, Piscataway, NJ 08854 USA
[4] Rice Univ, Dept Chem, Houston, TX 77005 USA
[5] Freie Univ, Dept Phys, D-14195 Berlin, Germany
基金
美国国家科学基金会;
关键词
MOLECULAR-DYNAMICS SIMULATIONS; STATE MODELS; KINETICS; REVEAL;
D O I
10.1021/acs.jctc.0c00991
中图分类号
O64 [物理化学(理论化学)、化学物理学];
学科分类号
070304 ; 081704 ;
摘要
The accurate sampling of protein dynamics is an ongoing challenge despite the utilization of high-performance computer (HPC) systems. Utilizing only "brute force" molecular dynamics (MD) simulations requires an unacceptably long time to solution. Adaptive sampling methods allow a more effective sampling of protein dynamics than standard MD simulations. Depending on the restarting strategy, the speed up can be more than 1 order of magnitude. One challenge limiting the utilization of adaptive sampling by domain experts is the relatively high complexity of efficiently running adaptive sampling on HPC systems. We discuss how the ExTASY framework can set up new adaptive sampling strategies and reliably execute resulting workflows at scale on HPC platforms. Here, the folding dynamics of four proteins are predicted with no a priori information.
引用
收藏
页码:7915 / 7925
页数:11
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