Distributed workflows for modeling experimental data

V. Lynch, Jose Borreguero Calvo, E. Deelman, Rafael Ferreira da Silva, Monojoy Goswami, Yawei Hui, E. Lingerfelt, J. Vetter
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引用次数: 1

Abstract

Modeling helps explain the fundamental physics hidden behind experimental data. In the case of material modeling, running one simulation rarely results in output that reproduces the experimental data. Often one or more of the force field parameters are not precisely known and must be optimized for the output to match that of the experiment. Since the simulations require high performance computing (HPC) resources and there are usually many simulations to run, a workflow is very useful to prevent errors and assure that the simulations are identical except for the parameters that need to be varied. The use of HPC implies distributed workflows, but the optimization and steps to compare the simulation results and experimental data are done on a local workstation. We will present results from force field refinement of data collected at the Spallation Neutron Source using Kepler, Pegasus, and BEAM workflows and discuss what we have learned from using these workflows.
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为实验数据建模的分布式工作流
建模有助于解释隐藏在实验数据背后的基本物理原理。在材料建模的情况下,运行一个模拟很少会产生再现实验数据的输出。通常一个或多个力场参数是不精确已知的,必须优化输出以匹配实验结果。由于模拟需要高性能计算(HPC)资源,并且通常有许多模拟要运行,工作流对于防止错误和确保模拟除了需要改变的参数外是相同的非常有用。HPC的使用意味着分布式工作流程,但优化和比较仿真结果和实验数据的步骤是在本地工作站完成的。我们将介绍使用Kepler、Pegasus和BEAM工作流程在散裂中子源收集的数据的力场细化结果,并讨论我们从使用这些工作流程中学到的东西。
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