Improving performance portability for GPU-specific OpenCL kernels on multi-core/many-core CPUs by analysis-based transformations

Mei Wen, Da-fei Huang, Chang-qing Xun, Dong Chen
School of Computer, National University of Defense Technology, Changsha 410073, China
Frontiers of Information Technology & Electronic Engineering


   title={Improving performance portability for GPU-specific OpenCL kernels on multi-core/many-core CPUs by analysis-based transformations},

   author={Mei, WEN and HUANG, Da-fei and XUN, Chang-qing and Dong, CHEN},



   publisher={Zhejiang University Press & Springer},



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OpenCL is an open heterogeneous programming framework. Although OpenCL programs are functionally portable, it does not provide performance portability, so code transformation often plays an irreplaceable role. When adapting GPU-specific OpenCL kernels to run on multi-core/many-core CPUs, coarsening the thread granularity is necessary and thus extensively used. However, locality concerns exposed in GPU-specific OpenCL code are usually inherited without analysis, which may give side-effects on the CPU performance. Typically, the use of OpenCL’s local memory on multi-core/many-core CPUs may lead to an opposite performance effect, because local-memory arrays no longer match well with the hardware and the associated synchronizations are costly. To solve this dilemma, we actively analyze the memory access patterns using array-access descriptors derived from GPU-specific kernels, which can thus be adapted for CPUs by 1) removing all the unwanted local-memory arrays together with the obsolete barrier statements and 2) optimizing the coalesced kernel code with vectorization and locality re-exploitation. Moreover, we have developed an automated tool chain that makes this transformation of GPU-specific OpenCL kernels into a CPU-friendly form, which is accompanied with a scheduler that forms a new OpenCL runtime. Experiments show that the automated transformation can improve OpenCL kernel performance on a multi-core CPU by an average factor of 3.24. Satisfactory performance improvements are also achieved on Intel’s Many-Integrated-Core coprocessor. The resultant performance on both architectures is better than or comparable with the corresponding OpenMP performance.
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