Collaborative Research: NSCI Framework. Software: SCALE-MS - Scalable Adaptive Large Ensembles of Molecular Simulations

合作研究：NSCI 框架。

基本信息

批准号：
1835780
负责人：
Peter Kasson
金额：
$ 76.33万
依托单位：
University of Virginia Main Campus
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2019
资助国家：
美国
起止时间：
2019-01-01 至 2023-12-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1835780&HistoricalAwards=false
关键词：
Collaborative Research NSCI Framework.Software

项目摘要

Molecular simulations are becoming important tools in understanding nanoscale processes in science and engineering. Such processes include the motions of proteins and nucleic acids that will enable design of better drugs, the interactions of liquids and metals in photovoltaic and catalytic applications, and the behavior of complex polymers used in industrial materials. Although national cyberinfrastructure investments are increasing raw computational power, the molecular timescales that scientists can simulate are not increasing proportionately. This means that most simulations are significantly shorter than the physical processes they are designed to study. Fortunately, many researchers have developed powerful algorithms that combine multiple simulations to overcome this molecular timescale problem, but these algorithms can still be very difficult to use effectively. This project, called SCALE-MS, will develop computing tools to simplify the process of writing algorithms that use large collections of molecular simulations to simulate the long timescales needed for scientific and industrial understanding. These tools will make it much simpler to have simulations interact adaptively, so simulation results can automatically guide the creation and running of new simulations. By making these complex multi-simulation algorithms easier to create and run, this project will enable users to run existing methods in computational molecular science more easily and make it possible for researchers to create and test new, even more powerful, methods for molecular modeling. This project also brings together researchers from biophysics, chemical engineering, and materials science, combining expertise from multiple simulation fields to develop important new ensemble simulation algorithms. This adaptive ensemble framework will enable communities of molecular simulation users in chemistry, chemical engineering, materials science, and biophysics to more easily exchange advanced methods and best practices. Many aspects of this framework can also be applied to aid societal problems requiring modeling in other domains, such as climate and earthquake modeling and prediction.This project addresses a fundamental need across molecular simulation communities from chemistry to biophysics to materials science: the ability to easily simulate long-timescale phenomena and slowly equilibrating ensembles. Researchers are increasingly developing high-level parallel algorithms that utilize simulation ensembles, loosely coupled molecular simulations that exchange information on a slower time scale than standard parallel computing techniques. However, most existing molecular simulation software cannot express ensemble simulation algorithms in a general manner and execute them at scale. There is thus a need for (i) the ability to express ensemble-based methods in a simple, easy- to-use manner that is agnostic of the underlying simulation code, (ii) support for adaptive and asynchronous execution of ensembles, and (iii) a scalable runtime system that encapsulates the complexity of executing and managing jobs seamlessly on different resources. The project will develop an extensible framework, including a simple high-level API and a sophisticated runtime system, to meet these design objectives on NSF?s production cyberinfrastructure. A key element of this design is the ability to specify ensemble-based patterns of work- and data-flow in a fashion independent of the challenges and complexity of the runtime management of the ensembles. This project will develop a framework consisting of a simple adaptive ensemble API with an underlying runtime platform that enables expression of ensemble simulation methods in a fashion agnostic of the underlying simulation code. This will facilitate design of new ensemble-based methods by the community and enable scientific end users to simply encode complex adaptive workflows. This approach separates the complexity of compute job management from the expression of sophisticated methods. The framework will support adaptive and asynchronous execution of ensembles, removing synchronization blocks that have restricted peta- and exa-scaling of simulation methods. This award by the Office of Advanced Cyberinfrastructure is jointly supported by the Division of Chemistry within the NSF Directorate for Mathematical and Physical Sciences and the Division of Chemical, Bioengineering, Environmental, and Transport Systems within the NSF Directorate for Engineering.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

分子模拟正在成为理解科学和工程中纳米级过程的重要工具。这些过程包括蛋白质和核酸的运动，这将有助于设计更好的药物，光伏和催化应用中液体和金属的相互作用，以及工业材料中使用的复杂聚合物的行为。尽管国家网络基础设施投资正在增加原始计算能力，但科学家可以模拟的分子时间尺度并没有相应增加。这意味着大多数模拟比它们旨在研究的物理过程要短得多。幸运的是，许多研究人员开发了强大的算法，结合多种模拟来克服这个分子时间尺度问题，但这些算法仍然很难有效使用。这个名为 SCALE-MS 的项目将开发计算工具来简化编写算法的过程，这些算法使用大量分子模拟来模拟科学和工业理解所需的长时间尺度。这些工具将使模拟自适应交互变得更加简单，因此模拟结果可以自动指导新模拟的创建和运行。通过使这些复杂的多重模拟算法更容易创建和运行，该项目将使用户能够更轻松地运行计算分子科学中的现有方法，并使研究人员能够创建和测试新的、甚至更强大的分子建模方法。该项目还汇集了来自生物物理学、化学工程和材料科学的研究人员，结合多个模拟领域的专业知识，开发重要的新集成模拟算法。这种自适应集成框架将使化学、化学工程、材料科学和生物物理学领域的分子模拟用户社区能够更轻松地交流先进方法和最佳实践。该框架的许多方面也可以应用于帮助解决需要在其他领域进行建模的社会问题，例如气候和地震建模和预测。该项目解决了从化学到生物物理学再到材料科学的分子模拟社区的基本需求：能够轻松地模拟长时标现象和缓慢平衡的系综。研究人员正在越来越多地开发高级并行算法，这些算法利用模拟集成、松散耦合的分子模拟，以比标准并行计算技术更慢的时间尺度交换信息。然而，大多数现有的分子模拟软件无法以通用方式表达集成模拟算法并大规模执行它们。因此，需要（i）能够以简单、易于使用的方式表达基于集成的方法，而与底层模拟代码无关，（ii）支持集成的自适应和异步执行，以及（ iii) 可扩展的运行时系统，封装了在不同资源上无缝执行和管理作业的复杂性。该项目将开发一个可扩展框架，包括一个简单的高级 API 和一个复杂的运行时系统，以满足 NSF 生产网络基础设施的这些设计目标。该设计的一个关键要素是能够以独立于集成运行时管理的挑战和复杂性的方式指定基于集成的工作和数据流模式。该项目将开发一个框架，该框架由简单的自适应集成 API 和底层运行时平台组成，该平台能够以与底层仿真代码无关的方式表达集成仿真方法。这将有助于社区设计新的基于集成的方法，并使科学最终用户能够简单地编码复杂的自适应工作流程。这种方法将计算作业管理的复杂性与复杂方法的表达分开。该框架将支持集成的自适应和异步执行，消除限制模拟方法的千万亿级和百亿亿级规模的同步块。该奖项由高级网络基础设施办公室颁发，并得到 NSF 数学和物理科学理事会化学部和 NSF 工程理事会化学、生物工程、环境和运输系统部的共同支持。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。