When considering different hardware platforms, not just the time-to-solution can be of importance but also the energy necessary to reach it. This is not only the case with battery powered and mobile devices but also with high-performance parallel cluster systems due to financial and practical limits on power consumption and cooling. Recent developments in hard- and software have given programmers the ability to run the same code on a range of different devices giving rise to the concept of heterogeneous computing. Many of these devices are optimized for certain types of applications. To showcase the differences and give a basic outlook on the applicability of different architectures for specific problems, the cross-platform OpenCL framework was used to compare both time- and energy-to-solution. A large set of devices ranging from ARM processors to server CPUs and consumer and enterprise level GPUs has been used with different benchmarking testcases taken from applied research applications. While the results show the overall advantages of GPUs in terms of both runtime and energy efficiency compared to CPUs, ARM devices show potential for certain applications in massively parallel systems. This study also highlights how OpenCL enables the use of the same codebase on many different systems and hardware platforms without specific code adaptations.

中文翻译：

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迈向绿色计算：使用 OpenCL 的不同平台的性能和能效调查

在考虑不同的硬件平台时，不仅解决时间很重要，而且达到它所需的能量也很重要。这不仅是电池供电和移动设备的情况，而且由于功耗和冷却的财务和实际限制，高性能并行集群系统也是如此。硬件和软件的最新发展使程序员能够在一系列不同的设备上运行相同的代码，从而产生了异构计算的概念。许多这些设备针对某些类型的应用程序进行了优化。为了展示差异并给出不同架构对特定问题的适用性的基本展望，跨平台 OpenCL 框架用于比较解决方案的时间和能量。从 ARM 处理器到服务器 CPU 以及消费级和企业级 GPU 的大量设备已与来自应用研究应用程序的不同基准测试用例一起使用。虽然结果显示 GPU 在运行时间和能源效率方面比 CPU 具有整体优势，但 ARM 设备在大规模并行系统中的某些应用程序中显示出潜力。这项研究还强调了 OpenCL 如何在许多不同的系统和硬件平台上使用相同的代码库，而无需进行特定的代码改编。ARM 设备在大规模并行系统中显示出某些应用的潜力。这项研究还强调了 OpenCL 如何在许多不同的系统和硬件平台上使用相同的代码库，而无需进行特定的代码改编。ARM 设备在大规模并行系统中显示出某些应用的潜力。这项研究还强调了 OpenCL 如何在许多不同的系统和硬件平台上使用相同的代码库，而无需进行特定的代码改编。