Research Computing support includes joint scientific and engineering research collaborations, HPC computational resources at South Carolina, middleware support for cluster and large scale computing, instructional tutorials, and access to XSEDE national supercomputer sites through the NSF Campus Champions program. Research Computing users include researchers both within the University of South Carolina system and neighboring institutions. This section features projects conducted by USC researchers.
Research Computing has been working closely with Dr. Sean Norman to optimize the Blast+ application for large runs of genomics data. In addition to achieving performance gains on University HPC resources, Research Computing has helped create customized code for large scale runs on NSF computational resources through our XSEDE partnerships.
I use the cluster for processing many MRI volumes of large size, that require several hours of processing per volume. The availability of the Bolden cluster and 120 dedicated cores has made this processing more efficient. I had 10 publications in the last two years, all that were based on work done on this cluster. The completed work, and the availability of the cluster, were directly responsible for my receipt of a 1 percentile review score for a recent NIH grant. I also received a 7 percentile score for another NIH grant for which analyses of new data with the cluster was 25 percent of the grant effort. For both grants, the cluster was an integral part of the research strategy and institutional support.
To enable simulations of complex systems that accurately reflect experimental observations, continued advances in modeling potential energy surfaces and statistical mechanical sampling are necessary. While studying systems relevant for catalysis, we develop new theoretical and computational tools for the investigation of these complex chemical systems. Our tool development efforts are at the interface between engineering, chemistry, and physics, and are rooted in classical, statistical, and quantum mechanics with a special focus on novel multiscale methods.
The Center for the Study of Aphasia Recovery uses brain imaging to guide brain stimulation, understand human language and help develop accurate prognoses for recovery following stroke. The combination of CPU clusters and GPU tuned applications dramatically accelerates our processing pipeline. Working with Research Computing, we have harnessed the clusters for optimal performance. My open source MRIcroGL and Surf Ice software allows you to view our results on any computer. More information about the Center for the Study of Aphasia Recovery, MRicroGL and Surf Ice can be found on the Neuropsychology Lab web site.