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

TL;DR

This paper presents a GPU-accelerated low-latency gravitational wave detection pipeline utilizing Maxwell GPU architecture features, achieving significant speed-ups over previous CPU and GPU implementations.

Contribution

The paper introduces a novel GPU implementation of SPIIR filtering leveraging Maxwell architecture, resulting in substantial performance improvements for gravitational wave searches.

Findings

3-fold speed-up over previous GPU implementation

Nearly 10-fold speed-up with new GPU thread configuration

Over 100-fold speed-up over single-core CPU for multi-rate filtering

Abstract

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 \textit{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.