基于NVIDIA CUDA的数字图像像素间滤波

J. Valero
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引用次数: 0

摘要

用复笛卡尔表示的数字图像的滤波过程允许使用可用的一维(1D)元素(像素间);然而,拥有这些额外的一维元素既增加了数据量,也增加了处理它们的时间。基于可用中央处理单元(cpu)数量的并行计算方案的时间减少策略不考虑额外的计算资源,例如NVIDIA的通用图形处理单元(gpu)提供的计算资源。探讨了NVIDIA gpu提供的并行计算可能性,并在此基础上,提出了一种利用NVIDIA公司gpu提供的应用程序接口开放计算语言(OpenCL)处理数字图像笛卡尔复数滤波任务的计算方案。通过将提出的解决方案的响应时间与仅使用CPU资源获得的响应时间进行比较,建立了结果评估。所获得的实现是过滤任务并行化的替代方案,它提供的响应时间比仅使用CPU资源的实现所获得的响应时间快14倍。NVIDIA多核GPU显著提高了并行性,可以与可用的多核CPU计算能力结合使用,同时使用这两种计算能力来平衡工作负载。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Inter-Pixel Filtrering of Digital Images with CUDA from NVIDIA
The process of filtering digital images represented by complex Cartesian allows to use the available onedimensional (1D) elements (interpixel); however, having those additional 1D elements increases both the volume of data and the time for processing them. The time reduction strategy based on a parallel computing scheme on the number of available central processing units (CPUs) does not consider additional computing resources such as those offered by general purpose graphics processing units (GPUs) of NVIDIA. Parallel computing possibilities provided by the NVIDIA GPUs were explored and, based on them, a computational scheme for the digital image Cartesian complexes filtering task was proposed using the application program interface Open Computing Language (OpenCL) provided for NVIDIA corporation GPUs. The results assessment was established by comparing the response times of the proposed solution compared to those obtained using only CPU resources. The obtained implementation is an alternative to parallelization of the filtering task, which provides response times up to 14 times faster than those obtained with the implementation that uses only the CPU resource. The NVIDIA multicore GPU significantly improves the parallelism, which can be used in conjunction with the available multicore CPU computing capacity, balancing the workload between these two computing powers using both simultaneously.
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