Extensible and Scalable Adaptive Sampling on Supercomputers
Extensible and Scalable Adaptive Sampling on Supercomputers
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DOI：
10.1021/acs.jctc.0c00991
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发表时间：
2020-12-08
影响因子：
5.5
通讯作者：
Clementi, Cecilia
中科院分区：
文献类型：
--
作者：
Hruska, Eugen;Balasubramanian, Vivekanandan;Clementi, Cecilia
蛋白质动力学的准确采样是一个持续的挑战，尽管利用高性能计算机（HPC）系统。仅利用“蛮力”分子动力学（MD）模拟需要不可接受的长时间来求解。自适应采样方法允许比标准MD模拟更有效的蛋白质动力学采样。取决于重启策略，速度可以超过1个数量级。限制领域专家利用自适应采样的一个挑战是在HPC系统上有效运行自适应采样的相对高的复杂性。我们讨论了ExTASY框架如何设置新的自适应采样策略，并在HPC平台上大规模可靠地执行由此产生的工作流。在这里，四个蛋白质的折叠动力学预测没有先验信息。
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.