Identifying volatile numeric expressions in numeric computing applications

Bayati, Mahsa; Leeser, Miriam; Gu, Yijia; Wahl, Thomas

doi:10.1016/j.matcom.2019.06.016

Cited by 2 publications

(1 citation statement)

References 2 publications

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“…OpenCL is composed of two parts: the language used to write the running code of the OpenCL device and the API related to the platform. A complete OpenCL program contains logical judgment functions running on CPU and computationally intensive functions (called kernel) running on GPU 29 . Each kernel is executed by multiple work-items at the same time, usually, 32 work-items constitute a warp, which is the basic unit of OpenCL execution.…”

Section: Algorithm Research and Analysismentioning

confidence: 99%

Unsharp masking image enhancement the parallel algorithm based on cross-platform

Song

Xiao

et al. 2022

Sci Rep

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In view of the low computational efficiency and the limitations of the platform of the unsharp masking image enhancement algorithm, an unsharp masking image enhancement parallel algorithm based on Open Computing Language (OpenCL) is proposed. Based on the analysis of the parallel characteristics of the algorithm, the problem of unsharp masking processing is implemented in parallel. Making use of the characteristics of data reuse in the algorithm, the effective allocation and optimization of global memory and constant memory are realized according to the access attributes of the data and the characteristics of the OpenCL storage model, and the use efficiency of off-chip memory is improved. Through the data storage access mode, the fast computing local memory access mode is discovered, and the logical data space transformation is used to convert the storage access mode, so as to improve the bandwidth utilization of the on-chip memory. The experimental results show that, compared with the CPU serial algorithm, the OpenCL accelerated unsharp masking image enhancement parallel algorithm greatly reduces the execution time of the algorithm while maintaining the same image quality, and achieves a maximum speedup of 16.71 times. The high performance and platform transplantation of the algorithm on different hardware platforms are realized. It provides a reference method for real-time processing of a large amount of data of high-resolution images for image enhancement.

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Section: Algorithm Research and Analysismentioning

confidence: 99%