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.
Star of Arkansas Supercomputer – a Key to Research Progress The NSF Major Research Instrumentation award #0722625 (PI Apon) was used to acquire the Star of Arkansas supercomputer. The Star of Arkansas was the key computational resource for the state and the region for more than three years. The Star of Arkansas helped to answer research questions and provide breakthroughs in several fields of application. Laurent Bellaiche, Narayani Choudury, and their team used the Star of Arkansas to discover an important characteristic of ferroelectric materials, called geometric frustration, that could lead to the development of new types of computer memory. An article published in Nature describes their results. Computational chemists Peter Pulay and Thomasz Janowski used the Star of Arkansas to model very large molecules, and then used the models to study processes that cause cancer. These processes cannot be studied without the use of high performance computing. Figure 1 shows a model of ethidium bormide between two DNA strands. Investigator Doug Spearot and his students used the Star of Arkansas to study new forms of metal alloys that can withstand extreme environmental stress such as extreme temperatures. In Figure 2, a view of dislocation nucleation in a Cu-0.2at.%Sb sample is shown. Magda El-Shenawee and her students developed computational models of breast tumors, based on the electric signals that are generated by cancerous cells. The models, which were run on the Star of Arkansas, can lead to new non-invasive methods for detection of breast cancer. Figure 3 shows the extracellular biopotential generated by a tumor composed of 1089 cells at two different stages. Jackson Cothren and Amy Apon and their students Stanislav Bobovych, Wesley Emeneker, and Seth Warn, used the Star of Arkansas to develop new methods for analyzing massive amounts of data that come from satellite images. These methods can be utilized to make agricultural practice more efficient, and to help to manage our natural resources. As a result of this MRI award, the use of high performance computing was greatly expanded at the University of Arkansas and with its regional partners. Hundreds of undergraduate and graduate students, faculty and researchers in Arkansas and its research partners were impacted by the Star of Arkansas, and more than six dozen publications were produced. The photo in Figure 4 shows Dr. Apon Amy and three of her Ph.D. graduates that have benefitted from the use of the supercomputer. From left to right, the photo includes Wesley Emeneker, Amy Apon, Hai Nguyen, and Linh Ngo. Credit for photos: University of Arkansas
