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

基本信息

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

项目摘要

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，引力波天体物理学、高能物理学和大规模电磁调查的网络基础设施需求迅速发展。在这些不同的研究领域中，用于实现科学发现的设施的建设和升级导致了一系列共同的巨大计算挑战：（i）数据集的复杂性和数量不断增加； (ii) 必须使用超额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天体粒子勘测的融合，即多信使天体物理学的诞生，已经让我们得以一睹它将在未来几年实现的变革性发现。鉴于大型强子对撞机 (LHC) 以及激光干涉仪引力波天文台 (LIGO) 和大型综合巡天望远镜 (LSST) 的组合在多信使天体物理学方面具有独特的科学发现潜力，社区需要加快开发和利用超越现有方法的深度学习算法。正如 NSF 的使命所述，该项目通过促进科学进步来服务于国家利益。它将大规模推动深度学习的前沿，展示这些方法的多功能性和可扩展性，以加速和实现大数据时代的新物理学。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密切互动，创造新的职业机会，并加强学术界和工业界之间的协同作用。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研讨会，对社区进行软件信用和软件引用方面的培训。在这个项目中，PI 将在我们最近的工作基础上开发高质量的深度学习作为开源软件，用于时间序列和图像数据集实时数据分析的学习算法。这项工作结合了可扩展的深度学习算法，使用数千个 GPU/CPU 在几分钟内使用 TB 大小的数据集进行训练，并采用最先进的方法来赋予确定性深度学习模型的预测完整的后验分布。该团队还将研究使用现场可编程门阵列（FPGA）来加速机器学习算法的低延迟推理，以最大限度地减少未来计算的需求，这是多信使天体物理学和粒子物理学的中心目标。作为这些活动的一部分而开发的开源工具将很容易与 LIGO、LHC 和 LSST 共享并被它们采用作为核心数据分析算法，这将显着提高现有算法的速度和深度，在需要最少计算的情况下实现新的物理学实时推理分析资源。该团队将组织深度学习研讨会和训练营，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。该团队将利用现有的开源和交互式模型存储库，例如阿贡的科学数据和学习中心 (DLHub)，来接触分析来自 LIGO、LHC 和 LSST 的开放数据集的大型社区。这将受益于使用这些需要最少计算资源来完成推理任务的技术。该项目得到了计算机与信息科学与工程局高级网络基础设施办公室和物理局的支持。数学和物理科学。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

期刊论文数量（13）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

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
影响因子：
0
作者：
S. Rosofsky;E. Huerta
通讯作者：
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
通讯作者：
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
影响因子：
0
作者：
Wei Wei-Wei;E. Huerta;Mengshen Yun;N. Loutrel;Md Arif Shaikh;Prayush Kumar;R. Haas;V. Kindratenko
通讯作者：
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