high performance computing on graphics processing units: hgpu.org

This paper explores the performance and energy efficiency of CUDA-enabled GPUs and multi-core SIMD CPUs using a set of kernels and full applications. Our implementations efficiently exploit both SIMD and thread-level parallelism on multi-core CPUs and the computational capabilities of CUDA-enabled GPUs. We discuss general optimization techniques for our CPU-only and CPU-GPU platforms. To fairly study performance and energy-efficiency, we also used two applications which utilize several kernels. Finally, we present an evaluation of the implementation effort required to efficiently utilize multi-core SIMD CPUs and CUDA-enabled GPUs for the benchmarks studied. Our results show that kernel-only performance and energy-efficiency could be misleading when evaluating parallel hardware; therefore, true results must be obtained using full applications. We show that, after all respective optimizations have been made, the best performing and energy-efficient platform varies for different benchmarks. Finally, our results show that PPEH (Performance gain Per Effort Hours), our newly introduced metric, can affectively be used to quantify efficiency of implementation effort across different benchmarks and platforms.