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Acceleration of low-latency gravitational wave searches using Maxwell-microarchitecture GPUs

Xiangyu Guo, Qi Chu, Shin Kee Chung, Zhihui Du, Linqing Wen
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}

}

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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.
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