The Square Kilometre Array (SKA) is an international initiative for developing the world's largest radio telescope with a total collecting area of over a million square meters. The scale of the operation, combined with the remote location of the telescope, requires the use of energy-efficient computational algorithms. This, along with the extreme data rates that will be produced by the SKA and the requirement for a real-time observing capability, necessitates in-situ data processing in an edge style computing solution. More generally, energy efficiency in the modern computing landscape is becoming of paramount concern. Whether it be the power budget that can limit some of the world's largest supercomputers, or the limited power available to the smallest Internet-of-Things devices. In this paper, we study the impact of hardware frequency scaling on the energy consumption and execution time of the Fast Fourier Transform (FFT) on NVIDIA GPUs using the cuFFT library. The FFT is used in many areas of science and it is one of the key algorithms used in radio astronomy data processing pipelines. Through the use of frequency scaling, we show that we can lower the power consumption of the NVIDIA V100 GPU when computing the FFT by up to 60% compared to the boost clock frequency, with less than a 10% increase in the execution time. Furthermore, using one common core clock frequency for all tested FFT lengths, we show on average a 50% reduction in power consumption compared to the boost core clock frequency with an increase in the execution time still below 10%. We demonstrate how these results can be used to lower the power consumption of existing data processing pipelines. These savings, when considered over years of operation, can yield significant financial savings, but can also lead to a significant reduction of greenhouse gas emissions.

中文翻译：

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边缘效率：提高 GPU 上 FFT 的能效以进行实时边缘计算

平方公里阵列（SKA）是一项国际倡议，旨在开发世界上最大的射电望远镜，总收集面积超过一百万平方米。操作的规模，结合望远镜的远程位置，需要使用节能计算算法。这一点，连同 SKA 将产生的极端数据速率和对实时观测能力的要求，需要在边缘计算解决方案中进行原位数据处理。更普遍的是，现代计算领域的能源效率正成为最受关注的问题。无论是可以限制某些世界上最大的超级计算机的功率预算，还是可用于最小的物联网设备的有限功率。在本文中，我们使用 cuFFT 库研究了硬件频率缩放对 NVIDIA GPU 上快速傅立叶变换 (FFT) 的能耗和执行时间的影响。FFT 用于许多科学领域，它是射电天文数据处理管道中使用的关键算法之一。通过使用频率缩放，我们表明在计算 FFT 时，与提升时钟频率相比，我们可以将 NVIDIA V100 GPU 的功耗降低多达 60%，而执行时间增加不到 10%。此外，对于所有测试的 FFT 长度使用一个通用内核时钟频率，与升压内核时钟频率相比，我们显示平均功耗降低了 50%，而执行时间的增加仍低于 10%。我们演示了如何使用这些结果来降低现有数据处理管道的功耗。考虑到多年的运营，这些节省可以产生显着的财务节省，但也可以导致温室气体排放量的显着减少。