DATA-DRIVEN PARALLELIZATION OF TIME IN MOLECULAR DYNAMICS SIMULATIONS

分子动力学模拟中数据驱动的时间并行化

• 批准号: 7723205
• 负责人: ASHOK SRINIVASAN
• 金额: $ 0.05万
• 依托单位: CARNEGIE-MELLON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-08-01 至 2009-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7723205
• 关键词: Algorithms; Class; Computer Retrieval of Information on Scientific Projects Database; Data; Funding; Future; Grant; Institution; Machine Learning; Modeling; Research; Research Personnel; Resources; Simulate; Source; Time; United States National Institutes of Health; base; molecular dynamics; novel strategies; simulation

This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. Conventional parallelization strategies do not scale well when the computational effort arises from the need to simulate to long time spans, rather than from large state space. Molecular Dynamics simulations constitute an important class of applications where this proves to be a bottleneck. We are developing a new approach to parallelization of Molecular Dynamics, which is based on the observation that simulations typically occur in a context rich in data from other related simulations. We use such data to parallelize the time domain, which yields a more scalable algorithm. This approach is based on the observation that long time-spans are often encountered in simulations with multiple time scales. The fine scales are responsible for the large computational effort. However, the important contribution of the fine-scales is often to the effect they have on the coarse scale. We use reduced order modeling to identify important coarse scale effects. We use clustering and machine learning to dynamically determine the relationship between the simulation being performed and prior data. This enables us to predict reasonable initial states at future times, which can be used to parallelize the time domain.

这个子项目是许多研究子项目中的一个 由NIH/NCRR资助的中心赠款提供的资源。子项目和 研究者（PI）可能从另一个NIH来源获得了主要资金， 因此可以在其他CRISP条目中表示。所列机构为 研究中心，而研究中心不一定是研究者所在的机构。 传统的并行化策略不能很好地扩展时，计算工作量来自于需要模拟到长的时间跨度，而不是从大的状态空间。分子动力学模拟构成了一类重要的应用，这被证明是一个瓶颈。我们正在开发一种新的方法来并行化分子动力学，这是基于观察，模拟通常发生在一个背景下，丰富的数据从其他相关的模拟。我们使用这样的数据来并行化时域，这产生了一个更可扩展的算法。这种方法是基于观察，长时间跨度经常遇到的模拟与多个时间尺度。精细的尺度导致了大量的计算工作。然而，细尺度的重要贡献往往是它们对粗尺度的影响。我们使用降阶建模来识别重要的粗尺度效应。我们使用聚类和机器学习来动态地确定正在执行的模拟与先验数据之间的关系。这使我们能够预测未来时间的合理初始状态，这可用于并行化时域。