high performance computing on graphics processing units: hgpu.org

Multi-core processors and a variety of accelerators have allowed scientific applications to scale to larger problem sizes. We present a performance, design methodology, platform, and architectural comparison of several application accelerators executing a Quantum Monte Carlo application. We compare the application’s performance and programmability on a variety of platforms including CUDA with Nvidia GPUs, Brook+ with ATI graphics accelerators, OpenCL running on both multi-core and graphics processors, C++ running on multi-core processors, and a VHDL implementation running on a Xilinx FPGA. We show that OpenCL provides application portability between multi-core processors and GPUs, but may incur a performance cost. Furthermore we illustrate that graphics accelerators can make simulations involving large numbers of particles feasible.