Purawat Shweta, Ieong Pek U, Malmstrom Robert D, Chan Garrett J, Yeung Alan K, Walker Ross C, Altintas Ilkay, Amaro Rommie E
San Diego Supercomputer Center, La Jolla, California; Workflows for Data Science Center of Excellence, San Diego Supercomputer Center, La Jolla, California; National Biomedical Computation Resource, University of California, La Jolla, California.
Department of Chemistry and Biochemistry, University of California, La Jolla, California; National Biomedical Computation Resource, University of California, La Jolla, California.
Biophys J. 2017 Jun 20;112(12):2469-2474. doi: 10.1016/j.bpj.2017.04.055.
With the drive toward high throughput molecular dynamics (MD) simulations involving ever-greater numbers of simulation replicates run for longer, biologically relevant timescales (microseconds), the need for improved computational methods that facilitate fully automated MD workflows gains more importance. Here we report the development of an automated workflow tool to perform AMBER GPU MD simulations. Our workflow tool capitalizes on the capabilities of the Kepler platform to deliver a flexible, intuitive, and user-friendly environment and the AMBER GPU code for a robust and high-performance simulation engine. Additionally, the workflow tool reduces user input time by automating repetitive processes and facilitates access to GPU clusters, whose high-performance processing power makes simulations of large numerical scale possible. The presented workflow tool facilitates the management and deployment of large sets of MD simulations on heterogeneous computing resources. The workflow tool also performs systematic analysis on the simulation outputs and enhances simulation reproducibility, execution scalability, and MD method development including benchmarking and validation.
随着朝着高通量分子动力学（MD）模拟的方向发展，这种模拟涉及在更长的、与生物学相关的时间尺度（微秒）上运行越来越多的模拟复制品，对改进计算方法以促进完全自动化的MD工作流程的需求变得更加重要。在这里，我们报告了一种用于执行AMBER GPU MD模拟的自动化工作流程工具的开发。我们的工作流程工具利用开普勒平台的功能，提供一个灵活、直观且用户友好的环境以及用于强大且高性能模拟引擎的AMBER GPU代码。此外，该工作流程工具通过自动化重复过程减少了用户输入时间，并便于访问GPU集群，其高性能处理能力使得大规模数值模拟成为可能。所展示的工作流程工具便于在异构计算资源上管理和部署大量的MD模拟。该工作流程工具还对模拟输出进行系统分析，并提高模拟的可重复性、执行可扩展性以及MD方法开发，包括基准测试和验证。
