GGrantIndex
← Search

MRI: Acquisition of a Supercomputing Cluster for Computational and Data-Intensive Applications in Science and Engineering

$815,286FY2007CSENSF

University Of Arkansas, Fayetteville AR

Investigators

Abstract

Proposal #: CNS 07-22625 PI(s): Apon, Amy W. Bellaiche, Laurent; Fu, Huaxiang; Pulay, Peter; Thompson. Craig W. Institution: University of Arkansas Fayettesville, AR 72701-1201 Title: MRI/Acq.: Supercomputer Cluster for Computational & Data-Intensive Applications in Science & Engineering Project Proposed: This project, acquiring a cluster for computational and data intensive applications, aims to enable projects in computer science, physics and chemistry, including . Grid Capacity Planning, focusing on grid workload and systems characterization and building simulation models or performance analysis, capacity planning, evaluation of scheduling, and data placement; . Approach to Design Low Loss, Tunable Ferroelectric Material, resolving dialectric loss at finite frequency and temperature for microscopic understanding of loss mechanism(s); . Discovering New Physics of Nanostructured Materials to understand and discover new physics in novel types of nanomaterials; . Developing Methods for Parallel Computing to Improve Fourier Transform Coulomb (FTC) and Efficient Implementation of Triple Substitutions in Coupled Cluster Theory; . Data Indexing and Middleware, including RFID middleware, Synthetic data generation service, Subsetting the workflow grid, VMlab, and Technology transition; . Computational Design of Self-assembly Systems for Nanostructured Formation; . High Performance Computing for Spray Cooling Modeling and Nanofluids; and . Atomistic Calculations of Interface Behavior in Nanostructured Materials. The computing cluster, an integrated supercomputing platform for distributed memory parallel applications, also enables collaborative courses in high-performance and grid computing. Broader Impact: The infrastructure contributes to outreach activities such as seminars and academic courses, attracting new users and building computational expertise in the region, as well as in the training of students.

View original record on NSF Award Search →