实时引力波天文学的硬件加速推断
Hardware-accelerated Inference for Real-Time Gravitational-Wave Astronomy
摘要：
瞬变天文学领域已经经历了一场革命，第一次引力波探测和多信使观测的到来使它们成为可能。由于首次探测到双黑洞和双中子星并合，引力波天文学的计算需求预计将在未来五年内至少增长两倍，因为全球网络的更大规模的干涉仪将达到设计灵敏度。随着探测器灵敏度的提高，作为多信使后续行动的推动因素，实时提供引力波警报将变得越来越重要。在这项工作中，我们报告了一种用于实时引力波数据去噪和天体物理源识别的深度学习推理的新实现和部署。这是使用通用的推理即服务模型来实现的，该模型能够适应未来对引力波数据分析的需求。我们的实现允许硬件加速器的无缝结合，也使商业或私人（专用）作为一个服务计算的使用。基于我们的研究结果，我们提出了一个范式转变的低延迟和离线计算的引力波天文学。这种转变可以解决峰值使用、可扩展性和可靠性方面的关键挑战，并提供一个特别针对深度学习应用优化的数据分析平台。实现的亚毫秒级延迟也将与任何基于机器学习的实时控制系统相关，这些系统可能在不久的将来和下一代地基激光干涉仪的操作中调用，以及来自此类仪器的数据的前端收集，分发和处理。
英文摘要：
The field of transient astronomy has seen a revolution with the first gravitational-wave detections and the arrival of multi-messenger observations they enabled. Transformed by the first detection of binary black hole and binary neutron star mergers, computational demands in gravitational-wave astronomy are expected to grow by at least a factor of two over the next five years as the global network of kilometer-scale interferometers are brought to design sensitivity. With the increase in detector sensitivity, real-time delivery of gravitational-wave alerts will become increasingly important as an enabler of multi-messenger followup. In this work, we report a novel implementation and deployment of deep learning inference for real-time gravitational-wave data denoising and astrophysical source identification. This is accomplished using a generic Inference-as-a-Service model that is capable of adapting to the future needs of gravitational-wave data analysis. Our implementation allows seamless incorporation of hardware accelerators and also enables the use of commercial or private (dedicated) as-a-service computing. Based on our results, we propose a paradigm shift in low-latency and offline computing in gravitational-wave astronomy. Such a shift can address key challenges in peak-usage, scalability and reliability, and provide a data analysis platform particularly optimized for deep learning applications. The achieved sub-millisecond scale latency will also be relevant for any machine learning-based real-time control systems that may be invoked in the operation of near-future and next generation ground-based laser interferometers, as well as the front-end collection, distribution and processing of data from such instruments.
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