Collaborative Research: Frameworks: Machine learning and FPGA computing for real-time applications in big-data physics experiments

合作研究：框架：大数据物理实验中实时应用的机器学习和 FPGA 计算

• 批准号: 1931561
• 负责人: Volodymyr Kindratenko
• 金额: $ 65.13万
• 依托单位: University of Illinois at Urbana-Champaign
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-10-01 至 2023-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1931561&HistoricalAwards=false
• 关键词: Collaborative; Research; Frameworks; Machine; learning

The cyberinfrastructure needs for gravitational wave astrophysics, high energy physics, and large-scale electromagnetic surveys have rapidly evolved in recent years. The construction and upgrade of the facilities used to enable scientific discovery in these disparate fields of research have led to a common pair of computational grand challenges: (i) datasets with ever-increasing complexity and volume; and (ii) data mining analyses that must be performed in real-time with oversubscribed computational resources. Furthermore, the convergence of gravitational wave astrophysics with electromagnetic and astroparticle surveys, the very birth of Multi-Messenger Astrophysics, has already provided a glimpse of the transformational discoveries that it will enable in years to come. Given the unique potential for scientific discovery with the Large Hadron Collider (LHC) and the combination of the Laser Interferometer Gravitational-wave Observatory (LIGO) and the Large Synoptic Survey Telescope (LSST) for Multi-Messenger Astrophysics, the community needs to accelerate the development and exploitation of deep learning algorithms that will outperform existing approaches. This project serves the national interest, as stated by NSF's mission, by promoting the progress of science. It will push the frontiers of deep learning at scale, demonstrating the versatility and scalability of these methods to accelerate and enable new physics in the big data era. Because these methods are also applicable to many other parts of our national and global economy and society, this work will positively impact many fields. The students and junior scientists to be mentored and trained in this research will interact closely with our industry partners, creating new career opportunities, and strengthening synergies between academia and industry. The team will share the algorithms with the community through open source software repositories, and through our tutorials and workshops the team will train the community regarding software credit and software citation.In this project, the PIs will build upon our recent work developing high quality deep learning algorithms for real-time data analytics of time-series and image datasets, as open source software. This work combines scalable deep learning algorithms, trained with TB-size datasets within minutes using thousands of GPUs/CPUs, with state-of-the-art approaches to endow the predictions of deterministic deep learning models with complete posterior distributions. The team will also investigate the use of Field Programmable Gate Arrays (FPGAs) to accelerate low-latency inference of machine learning algorithms to minimize the demands of future computing, which is a central goal for Multi-Messenger Astrophysics and particle physics. The open source tools to be developed as part of these activities will be readily shared with and adopted by LIGO, LHC, and LSST as core data analytics algorithms that will significantly increase the speed and depth of existing algorithms, enabling new physics while requiring minimal computational resources for real-time inferences analyses. The team will organize deep learning workshops and bootcamps to train students and researchers on how to use and contribute to our framework, creating a wide network of contributors and developers across key science missions. The team will leverage existing open source and interactive model repositories, such as the Data and Learning Hub for Science (DLHub) at Argonne, to reach out to a large cross-section of communities that analyze open datasets from LIGO, LHC, and LSST, and that will benefit from the use of these technologies that require minimal computational resources for inference tasks.This project is supported by the Office of Advanced Cyberinfrastructure in the Directorate for Computer & Information Science & Engineering and the Division of Physics in the Directorate of Mathematical and Physical Sciences.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

近年来，网络基础设施对引力波天体物理学，高能量物理学和大规模电磁调查的需求迅速发展。用于在这些不同研究领域实现科学发现的设施的构建和升级导致了一对计算大挑战：（i）具有越来越多的复杂性和数量的数据集； （ii）必须通过超额订购的计算资源实时执行的数据挖掘分析。此外，引力波天体物理学与电磁和星星颗粒调查的收敛性是多通信天体物理学的诞生，已经瞥见了它将在未来几年内启用的变革性发现。鉴于与大型强子对撞机（LHC）的科学发现以及激光干涉仪重力波动台（LIGO）和大型Synoptic调查望远镜（LSST）的独特潜力，用于多际交往者天文学的望远镜（LSST），社区需要加快对深度学习Algorithms的发展和利用，这将超过现有的ALGORITH MONDISS现有方法。正如NSF的使命所述，该项目通过促进科学进步来实现国家利益。它将扩大深度学习的边界，证明这些方法在大数据时代加速和启用新物理学的多功能性和可扩展性。由于这些方法也适用于我们国家和全球经济和社会的许多其他部分，因此这项工作将对许多领域产生积极影响。在这项研究中接受指导和培训的学生和初级科学家将与我们的行业合作伙伴紧密互动，创造新的职业机会，并加强学术界与行业之间的协同作用。该团队将通过开源软件存储库与社区共享算法，并通过我们的教程和讲习班，团队将培训社区有关软件信用和软件引用的培训。在该项目中，PIS将基于我们最近开发的高质量深度学习算法，用于实时数据分析的高质量深度学习算法，以实时数据分析，以及开放量量软件。这项工作结合了可扩展的深度学习算法，使用数千个GPU/CPU在几分钟内接受TB大小数据集训练，并采用最新的方法来赋予确定性深度学习模型的预测，并具有完整的后验分布。该团队还将调查使用现场可编程栅极阵列（FPGA）来加速机器学习算法的低延迟推断，以最大程度地减少未来计算的需求，这是多童天天体物理学和粒子物理学的核心目标。 Ligo，LHC和LSST作为核心数据分析算法将很容易地共享并采用作为这些活动的一部分开发的开源工具，这些算法将大大提高现有算法的速度和深度，同时促进新的物理学，同时需要最小的计算资源来进行实时推理分析。该团队将组织深度学习研讨会和训练营，以培训学生和研究人员如何使用和为我们的框架做出贡献，从而在关键科学任务中创建广泛的贡献者和开发人员网络。团队将利用现有的开源和交互式模型存储库，例如Argonne的数据和学习中心（DLHUB），与大量的社区接触，这些社区分析了从LIGO，LHC和LSST分析开放数据集的大量社区，这将受益于这些cy cy for intervication intervications intervications inpections inpertive inpectionssss.对于计算机和信息科学与工程以及数学和物理科学局的物理部门，该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛的影响评估标准来通过评估来支持的。

项目成果

Magnetohydrodynamics with physics informed neural operators

• DOI: 10.1088/2632-2153/ace30a
• 发表时间: 2023-02
• 期刊: Machine Learning: Science and Technology
• 作者: S. Rosofsky;E. Huerta

Numerical relativity higher order gravitational waveforms of eccentric, spinning, nonprecessing binary black hole mergers

• DOI: 10.1103/physrevd.107.064038
• 发表时间: 2022-10
• 期刊: Physical Review D
• 影响因子: 5
• 作者: A. Joshi;S. Rosofsky;R. Haas;E. Huerta

Enabling real-time multi-messenger astrophysics discoveries with deep learning

• DOI: 10.1038/s42254-019-0097-4
• 发表时间: 2019-10-01
• 期刊: NATURE REVIEWS PHYSICS
• 影响因子: 38.5
• 作者: Huerta, E. A.;Allen, Gabrielle;Zhao, Zhizhen
• 通讯作者: Zhao, Zhizhen

Deep Learning with Quantized Neural Networks for Gravitational-wave Forecasting of Eccentric Compact Binary Coalescence

• DOI: 10.3847/1538-4357/ac1121
• 发表时间: 2020-12
• 期刊: The Astrophysical Journal
• 作者: Wei Wei-Wei;E. Huerta;Mengshen Yun;N. Loutrel;Md Arif Shaikh;Prayush Kumar;R. Haas;V. Kindratenko

Observation of eccentric binary black hole mergers with second and third generation gravitational wave detector networks

• DOI: 10.1103/physrevd.103.084018
• 发表时间: 2020-08
• 期刊: Physical Review D
• 影响因子: 5
• 作者: Zhuo Chen;E. Huerta;Joseph Adamo;R. Haas;É. O’Shea;Prayush Kumar;C. Moore