A Comparison of xPU Platforms Exemplified with Ray Tracing Algorithms

Rafael Huff, T. Gierlinger, Arjan Kuijper, A. Stork, D. Fellner
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Abstract

Over the years, faster hardware - with higher clock rates - has been the usual way to improve computing times in computer graphics. Aside from highly costly parallel solutions only affordable by big industries - like the movie industry -, there was no alternative available to desktop users. Nevertheless, this scenario is dramatically changing with the introduction of more and more parallelism in current desktop PCs. Multi-core CPUs are a common basis in current PCs and the power of modern GPUs - which have been multi-core for a long time now - is getting unveiled to developers. nVidia's CUDA is a powerful weapon to explore GPUs parallelism. Yet, its specific target - nVidia graphic cards only - does not provide any solution to other parallel hardware present. OpenCL is a new royalty-free cross-platform intended to be portable across different hardware manufacturers or even different platforms.In this paper we focus on a comparison of advantages and disadvantages of xPU platforms with OpenCL and CUDA in terms of time efficiency. As an example application we use ray tracing algorithms. Three kinds of ray tracers have to be developed in order to conduct a fair comparison: one is CPU based, while the other two are GPU based - using CUDA and OpenCL, respectively. At the end, a comparison is done between them and results are presented and analyzed showing that the CUDA implementation has the best frame rate, but is very closely followed by the OpenCL implementation. Visually, results are identical, showing the high potential of OpenCL as an alternative for CUDA with identical performance.
以光线追踪算法为例的xPU平台比较
多年来,更快的硬件——更高的时钟速率——一直是提高计算机图形计算时间的常用方法。除了昂贵的并行解决方案只有大型行业(如电影行业)才能负担得起之外,桌面用户没有其他选择。然而,随着当前桌面pc中越来越多的并行性的引入,这种情况正在发生巨大的变化。多核cpu是当前个人电脑的常见基础,而现代gpu的强大功能——多核gpu已经存在很长时间了——正在向开发人员展示。nVidia的CUDA是探索gpu并行性的强大武器。然而,它的特定目标- nVidia显卡-不提供任何解决方案,其他并行硬件存在。OpenCL是一种新的免版税跨平台,旨在跨不同硬件制造商甚至不同平台进行移植。在本文中,我们重点比较了xPU平台与OpenCL和CUDA在时间效率方面的优缺点。作为一个示例应用,我们使用光线追踪算法。为了进行公平的比较,必须开发三种光线追踪器:一种是基于CPU的,而另外两种是基于GPU的-分别使用CUDA和OpenCL。最后,对它们进行了比较,并对结果进行了分析,表明CUDA实现具有最佳帧率,但紧随其后的是OpenCL实现。从视觉上看,结果是相同的,显示了OpenCL作为CUDA替代品的高潜力,具有相同的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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