Cunningham, AdamPayton, GeraldSlettebak, JackPou, Jordi WolfsonGraf, JonathanHuang, XuanKhuvis, SamuelGobbert, Matthias K.Salter, ThomasMountain, David J.2018-10-012018-10-012014http://hdl.handle.net/11603/11406Parallelization of code, using multiple cores/threads, and heterogeneous computing, using the CPU with other devices, has come to the forefront of computing as methods to reduce the execution time of computationally demanding algorithms. For our project, we test various hardware setups on the maya cluster at UMBC, which include multiple nodes and GPUs, by solving the Poisson equation using the conjugate gradient method. To explore these different setups, we made use of both industry benchmarks and our own code, which we design using the compilers native to each device and API. We fi nd significant gains both in using a heterogeneous model and after parallelizing our code.20 pagesen-USThis item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.heterogeneous computingGPUsconjugate gradient methodParallelizationUMBC High Performance Computing Facility (HPCF)multiple cores/threadsparallel computing codePoisson equationText