The CUDA Research Center in the Computer Science Department at the University of Georgia was established in April 2014. The CUDA Research Center in collaboration with the CUDA Teaching Center will be offering workshops and training courses for students, postdocs, and faculty. There are a good number of research projects underway that uses GPUs such as the Biological Clock, Biochemical Reaction Networks, Machine Learning, Brain Mapping, Quantum Chemistry Algorithms, and Problems in Condensed Matter Physics. Many other research projects such as Pattern Recognition, Optical Fiber Communication Systems, and Water Waves will be started that will utilize the high performance of the GPUs. Several research papers from the above research projects have already been published.
The Center offers researchers a dedicated lab with twelve Nvidia GeForce GTX 480 GPUs connected by a six-host interconnection network, a website with access to tutorials and learning resources, and hands-on training including but not limited to invitational seminars since 2012. In addition, UGA researchers have access to the Georgia Advanced Computing Resource Center (GACRC), which installed thirty-two Tesla K20X Kepler GPUs and nine Tesla M2070 Fermi GPUs in May 2013 and this is due in part to the effort of Dr. Taha and the CUDA Teaching Center.
In January of 2014, a large group of over 40 scientists at UGA submitted an MRI grant proposal to NSF to purchase a CPU/GPU High Performance Computing system for "Research and Training at The Interface of The Biological and Physical Sciences." Prof. Taha was one of the Co-Investigators on this proposal. In summary, there are a growing number of researchers from a good number of departments and centers at UGA that either have used and continue to use GPUs or will start using the GPUs in their research. Because of the growing demand from the research community at UGA, the GACRC will invest a good amount of funds to purchase more GPUs in the coming few months regardless of the funding from NSF.
Numerical Simulations in Science and Engineering
CUDA C Programming on GPUs for High Performance Computing
Advanced Numerical Methods and Scientific Computing