This is an Institutional Infrastructure proposal to build a GPU cluster to support research in data-parallel code development and optimization, as well as research applications, in three scientific domains, namely, seismology, biology and astrophysics. These goals build on a close collaboration with an expert team in GPU computing from computer science. The proposed cluster will serve not only as an invaluable resource for computation, but will also aid cross-fostering of techniques and concepts between disciplines and will be used to stimulate collaboration and synergistic research activity in a wide range of areas.
Even though domain scientists are increasingly dependent on computation to achieve their research goals, most are not experts in parallel programming or GPU architectures. The difficulty of parallel programming for GPU clusters is an impediment to scientific progress. In order to relieve scientists of the burdens of parallel programming, computer scientists at Princeton have developed systems for automatically parallelizing programs for GPU. Building on this success, the PIs plan to extend these techniques to GPU clusters and work closely with the seismologists, biologists and astrophysicists to accelerate the pace of science.
