Acceleration of low-latency gravitational wave searches using Maxwell-microarchitecture GPUs
Tsinghua National Laboratory for Information Science and Technology, Department of Computer Science and Technology, Tsinghua University, Beijing, 100084, China
arXiv:1702.02256 [astro-ph.IM], (8 Feb 2017)
@article{guo2017acceleration,
title={Acceleration of low-latency gravitational wave searches using Maxwell-microarchitecture GPUs},
author={Guo, Xiangyu and Chu, Qi and Chung, Shin Kee and Du, Zhihui and Wen, Linqing},
year={2017},
month={feb},
archivePrefix={"arXiv"},
primaryClass={astro-ph.IM}
}
Low-latency detections of gravitational waves (GWs) are crucial to enable prompt follow-up observations to astrophysical transients by conventional telescopes. We have developed a low-latency pipeline using a technique called Summed Parallel Infinite Impulse Response (SPIIR) filtering, realized by a Graphic Processing Unit (GPU). In this paper, we exploit the new Maxwell memory access architecture in NVIDIA GPUs, namely the read-only data cache, warp-shuffle, and cross-warp atomic techniques. We report a 3-fold speed-up over our previous implementation of this filtering technique. To tackle SPIIR with relatively few filters, we develop a new GPU thread configuration with a nearly 10-fold speedup. In addition, we implement a multi-rate scheme of SPIIR filtering using Maxwell GPUs. We achieve more than 100-fold speed-up over a single core CPU for the multi-rate filtering scheme. This results in an overall of 21-fold CPU usage reduction for the entire SPIIR pipeline.
February 10, 2017 by hgpu
