An approach of performance comparisons with OpenMP and CUDA parallel programming on multicore systems

In the past, the tenacious semiconductor problems of operating temperature and power consumption limited the performance growth for single-core microprocessors. Microprocessor vendors hence adopt the multicore chip organizations with parallel processing because the new technology promises faster and lower power needed. In a short time, this trend floods first the development of CPU, then also the other peripherals like GPU. Modern GPUs are very efficient in manipulating computer graphics, and their highly parallel structure makes them even more effective than general-purpose CPUs for a range of graphical complex algorithms. However, technology of multicore processor brought revolution and unavoidable collision to the programming personnel. Multicore processor has high performance; however, parallel processing brings not only the opportunity but also a challenge. The issue of efficiency and the way how programmer or compiler parallelizes the software explicitly are the keys that enhance the performance on multicore chip. In this paper, we propose a parallel programming approach using hybrid CUDA, OpenMP, and MPI programming. There would be two verificational experiments presented in the paper. In the first, we would verify the availability and correctness of the auto-parallel tools, and discuss the performance issues on CPU, GPU, and embedded system. In the second, we would verify how the hybrid programming could surely improve performance. Copyright (C) 2016 John Wiley & Sons, Ltd.