Collaborative Research: The Power of Many: Scalable Compute and Data-Intensive Science on Blue Waters

协作研究：多人的力量：蓝水域的可扩展计算和数据密集型科学

• 批准号: 1516469
• 负责人: Shantenu Jha
• 金额: $ 1.5万
• 依托单位: Rutgers University New Brunswick
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-08-01 至 2016-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1516469&HistoricalAwards=false
• 关键词: Collaborative; Research; Power; Many; Scalable

There is a critical need to solve problems of societal and technical importance faster and at larger scales than is currently possible. This project will support several new applications to utilize the Blue Waters Supercomputer at scales that are scientifically needed but simply not possible otherwise or elsewhere. These include applications ranging from protein conformations to polar sciences. If successful, this research will engender a significant step up in capabilities towards extreme scale computing and data-intensive science.The computational resources made available as part of this project will enable the design, development and testing of multiple new algorithms, middleware and methods. In the first track, the researchers propose a new approach to characterize the conformational landscape of the NMDAr LBD in its different forms, by using novel sampling methodologies and workload management tools. Leveraging sophisticated sampling methods, simulations will provide an unprecedented atomically detailed picture of the different states the protein can adopt as a function of the ligands it interacts with, and will also provide predictions of the kinetic pathways that link these states together. The result will be a conceptual framework to understand the sometimes perplexing experimental results and a springboard for the rational design of further experiments.This study will open the way to a conceptually different approach to studying large conformational changes in complex macromolecules, as the same methodology and computational infrastructure can in principle be applied to a large number of biomedically relevant systems. The second track of this project is concerned with development, scaling and optimization of SPIDAL (Scalable Parallel Interoperable Data Intensive Libraries) and MIDAS (Middleware for Data-intensive Analysis and Science) that will be used to enhance the scalability of a range of data-intensive applications.

在更快的范围内解决社会和技术重要性问题的迫切需要比目前的可能性更大。该项目将支持几个新应用程序以科学所需的规模利用蓝色水域超级计算机，但根本不可能或其他地方。这些包括从蛋白质构象到极性科学的应用。如果成功的话，这项研究将在极端规模的计算和数据密集型科学方面取得重大提升。作为该项目的一部分，提供的计算资源将使多种新算法，中间件和方法的设计，开发和测试能够实现。在第一条曲目中，研究人员提出了一种新的方法，以使用新颖的采样方法和工作量管理工具来表征NMDAR LBD不同形式的构象格局。利用复杂的抽样方法，模拟将提供前所未有的原子详细图像，以蛋白质可以作为与其相互作用的配体的函数所采用的不同状态，并还将提供将这些状态联系在一起的动力学途径的预测。结果将是一个概念框架，以了解有时令人困惑的实验结果和用于进一步实验的合理设计的跳板。这项研究将为研究复杂的大分子的大构象变化提供概念上不同的方法，因为相同的方法论和计算基础结构可以原则上适用于大量相关的生物学系统。该项目的第二个轨道与将用于增强一系列数据密集型应用程序的可扩展性的蜘蛛（可扩展可互操作数据密集型库）和MIDAS（用于数据密集型分析和科学的中间软件）的开发，扩展和优化有关。