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.

迫切需要比目前可能的更快和更大规模地解决具有社会和技术重要性的问题。这个项目将支持几个新的应用程序，利用蓝色沃茨超级计算机在科学上需要的规模，但根本不可能在其他地方或其他地方。这些包括从蛋白质构象到极性科学的应用。如果成功，这项研究将在极端规模计算和数据密集型科学的能力上迈出重要一步。作为该项目的一部分，可获得的计算资源将使多个新算法，中间件和方法的设计，开发和测试成为可能。在第一轨道中，研究人员提出了一种新的方法来表征NMDAR LBD在其不同形式的构象景观，通过使用新的采样方法和工作量管理工具。利用复杂的采样方法，模拟将提供蛋白质可以采用的不同状态的前所未有的原子详细图像，作为与其相互作用的配体的函数，并且还将提供将这些状态连接在一起的动力学途径的预测。其结果将是一个概念框架，以理解有时令人困惑的实验结果和跳板的合理设计进一步的实验。这项研究将开辟一条道路，以一个概念上不同的方法来研究复杂的大分子的大构象变化，作为相同的方法和计算基础设施，原则上可以应用到大量的生物医学相关的系统。该项目的第二个轨道是关于SPIDAL（可扩展并行互操作数据密集型库）和MIDAS（数据密集型分析和科学中间件）的开发，扩展和优化，将用于增强一系列数据密集型应用程序的可扩展性。