Using the Fourier Domain Acceleration Search (FDAS) method to search for binary pulsars is a computationally costly process. Next generation radio telescopes will have to perform FDAS in real time, as data volumes are too large to store. FDAS is a matched filtering approach for searching time-domain radio astronomy datasets for the signatures of binary pulsars with approximately linear acceleration. In this paper we will explore how we have reduced the energy cost of an SKA-like implementation of FDAS in AstroAccelerate, utilising a combination of mixed-precision computing and dynamic frequency scaling on NVIDIA GPUs. Combining the two approaches, we have managed to save 58% of the overall energy cost of FDAS with a (<3%) sacrifice in numerical sensitivity.
翻译:使用 Fourier 域加速搜索法( FDAS ) 搜索二元脉冲星是一个计算成本高昂的过程。 下一代射电望远镜将不得不实时运行 FDIS, 因为数据量太大, 无法储存。 FDIAS 是一种匹配的过滤方法, 用于搜索时间- 域射电天文学数据集, 以近线性加速的二元脉冲星的签名。 在本文件中, 我们将探讨我们如何降低SKA式的在天体加速度实施FDAS的能源成本, 将混合精度计算和动态频率缩放相结合, 在 NVIDIA GPUs 上使用混合精度计算和动态频率缩放。 结合这两种方法, 我们设法节省了FDISA 系统总能源成本的58%, 并在数字敏感度下( < 3 % ) 的牺牲。