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.
翻译:平方千米阵列( SKA) 是开发世界上最大的射电望远镜的国际举措, 其总收集面积超过100万平方米。 操作规模, 加上望远镜的远程位置, 需要使用节能计算算算算算法。 这加上SKA将产生的极端数据率以及实时观测能力的要求, 需要用边缘式计算解决方案进行现场数据处理。 更普遍地说, 现代计算环境中的能源效率正在成为最令人担心的问题。 不管是能限制世界最大超级计算机某些频率的电力预算, 还是能限制最小的互联网电话装置的有限电流。 在本文中, 我们研究的是快速四面变换(FFT)的硬件频率对能源消耗和执行时间的影响。 FFFT仍然用于许多科学领域, 但它是射电天文学数据处理管道中所使用的关键算法之一。 通过使用频率缩放速度的频率, 与最小的互联网- 网络- 设备运行的频率相比, 我们可以用50个频率的频率递减速度来降低目前GFDFA的耗量。