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.

迫切需要比目前更快、更大规模地解决具有社会和技术重要性的问题。该项目将支持多种新应用程序，以科学上需要的规模利用 Blue Waters 超级计算机，但在其他地方或其他地方根本不可能实现。其中包括从蛋白质构象到极地科学的应用。如果成功，这项研究将显着提高超大规模计算和数据密集型科学的能力。作为该项目的一部分提供的计算资源将支持多种新算法、中间件和方法的设计、开发和测试。在第一个轨道中，研究人员提出了一种新方法，通过使用新颖的采样方法和工作量管理工具来表征不同形式的 NMDar LBD 的构象景观。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。结果将成为理解有时令人困惑的实验结果的概念框架，并为进一步实验的合理设计提供跳板。这项研究将为研究复杂大分子中大构象变化的概念上不同的方法开辟道路，因为相同的方法和方法计算基础设施原则上可以应用于大量生物医学相关系统。该项目的第二个轨道涉及 SPIDAL（可扩展并行可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，它们将用于增强一系列数据的可扩展性密集应用。