%0 Conference Paper %1 Shen2012-oe %A Shen, Jie %A Fang, Jianbin %A Sips, H %A Varbanescu, A L %B Parallel Processing Workshops (ICPPW), 2012 41st International Conference on %D 2012 %I ieeexplore.ieee.org %K Arrays CPU Expose Hardware Kernel Multi-core OpenCL OpenCL_compilers OpenCL_fine-grained_parallelism OpenMP Parallel_processing Performance_Comparison Performance_evaluation Programming Rodinia_benchmark_suite Thread_scheduling Vectorization multicore_CPU multicore_processors multiprocessing_programs multiprocessing_systems performance_gaps program_compilers programming_models %P 116--125 %T Performance Gaps between OpenMP and OpenCL for Multi-core CPUs %X OpenCL and OpenMP are the most commonly used programming models for multi-core processors. They are also fundamentally different in their approach to parallelization. In this paper, we focus on comparing the performance of OpenCL and OpenMP. We select three applications from the Rodinia benchmark suite (which provides equivalent OpenMP and OpenCL implementations), and carry out experiments with different datasets on three multi-core platforms. We see that the incorrect usage of the multi-core CPUs, the inherent OpenCL fine-grained parallelism, and the immature OpenCL compilers are the main reasons that lead to the OpenCL poorer performance. After tuning the OpenCL versions to be more CPU-friendly, we show that OpenCL either outperforms or achieves similar performance in more than 80\% of the cases. Therefore, we believe that OpenCL is a good alternative for multi-core CPU programming.

@inproceedings{Shen2012-oe, abstract = {OpenCL and OpenMP are the most commonly used programming models for multi-core processors. They are also fundamentally different in their approach to parallelization. In this paper, we focus on comparing the performance of OpenCL and OpenMP. We select three applications from the Rodinia benchmark suite (which provides equivalent OpenMP and OpenCL implementations), and carry out experiments with different datasets on three multi-core platforms. We see that the incorrect usage of the multi-core CPUs, the inherent OpenCL fine-grained parallelism, and the immature OpenCL compilers are the main reasons that lead to the OpenCL poorer performance. After tuning the OpenCL versions to be more CPU-friendly, we show that OpenCL either outperforms or achieves similar performance in more than 80\% of the cases. Therefore, we believe that OpenCL is a good alternative for multi-core CPU programming.}, added-at = {2015-04-10T18:02:47.000+0200}, author = {Shen, Jie and Fang, Jianbin and Sips, H and Varbanescu, A L}, biburl = {https://www.bibsonomy.org/bibtex/2a631c2503b1619e86eb0c953d685aaa6/christophv}, booktitle = {Parallel Processing Workshops ({ICPPW)}, 2012 41st International Conference on}, interhash = {c46c412c4aa9d37f4a497fcf61507240}, intrahash = {a631c2503b1619e86eb0c953d685aaa6}, keywords = {Arrays CPU Expose Hardware Kernel Multi-core OpenCL OpenCL_compilers OpenCL_fine-grained_parallelism OpenMP Parallel_processing Performance_Comparison Performance_evaluation Programming Rodinia_benchmark_suite Thread_scheduling Vectorization multicore_CPU multicore_processors multiprocessing_programs multiprocessing_systems performance_gaps program_compilers programming_models}, pages = {116--125}, publisher = {ieeexplore.ieee.org}, timestamp = {2016-01-04T14:22:08.000+0100}, title = {Performance Gaps between {OpenMP} and {OpenCL} for Multi-core {CPUs}}, year = 2012 }