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

迫切需要比目前可能的更快和更大规模地解决社会和技术上的重要问题。该项目将支持几个新的应用，以大规模利用蓝水超级计算机，这些应用在科学上是需要的，但在其他地方是不可能的。这些应用范围从蛋白质构象到极地科学。如果成功的话，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的一步。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、开发和测试成为可能。在第一个轨道上，研究人员提出了一种新的方法，通过使用新的采样方法和工作量管理工具来表征NMDAr LBD不同形式的构象景观。利用复杂的采样方法，模拟将提供前所未有的蛋白质不同状态的原子细节图，作为它与配体相互作用的函数，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。结果将是一个概念性框架，以理解有时令人困惑的实验结果，并为进一步的实验的合理设计的跳板。这项研究将为研究复杂大分子的大构象变化开辟一种概念上不同的方法，因为相同的方法和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件）的开发、扩展和优化，这将用于增强一系列数据密集型应用程序的可扩展性。

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.