COLLABORATIVE RESEARCH: THE POWER OF MANY: SCALABLE COMPUTE AND DATA-INTENSIVE SCIENCE ON BLUE WATERS

Project Details

Description

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.
StatusFinished
Effective start/end date8/1/157/31/16

Funding

  • National Science Foundation (National Science Foundation (NSF))

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