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:
- 金额:$ 65.13万
- 依托单位:
- 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:
- 关键词:
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。用于在这些不同的研究领域实现科学发现的设施的建设和升级导致了一对共同的计算挑战:(i)复杂性和数量不断增加的数据集;以及(ii)必须在超额订阅的计算资源下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调查的融合,多信使天体物理学的诞生,已经提供了它将在未来几年实现的变革性发现的一瞥。鉴于大型强子对撞机(LHC)以及激光干涉引力波天文台(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,社区需要加速开发和利用深度学习算法,以超越现有方法。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,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库与社区分享算法,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将建立在我们最近的工作基础上,开发高质量的深度学习算法,用于时间序列和图像数据集的实时数据分析,作为开源软件。这项工作结合了可扩展的深度学习算法,使用数千个GPU/CPU在几分钟内使用TB大小的数据集进行训练,并采用最先进的方法为确定性深度学习模型的预测提供完整的后验分布。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核心目标。作为这些活动的一部分,将开发的开源工具将很容易与LIGO,LHC和LSST共享并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,实现新的物理学,同时需要最少的计算资源进行实时推理分析。该团队将组织深度学习研讨会和训练营,培训学生和研究人员如何使用我们的框架并为之做出贡献,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,如阿贡的科学数据和学习中心(DLHub),以接触分析LIGO,LHC和LSST开放数据集的大型跨部门社区,这将受益于使用这些技术,这些技术需要最少的计算资源来进行推理任务。该项目由高级网络基础设施办公室支持计算机&信息科学&工程理事会和数学与物理科学理事会物理部。该奖项反映了NSF的法定使命,并且通过使用基金会的知识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:
- 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:
- 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
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:
- 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
- DOI:10.1103/physrevd.103.084018
- 发表时间:2020-08
- 期刊:
- 影响因子:5
- 作者:Zhuo Chen;E. Huerta;Joseph Adamo;R. Haas;É. O’Shea;Prayush Kumar;C. Moore
- 通讯作者:Zhuo Chen;E. Huerta;Joseph Adamo;R. Haas;É. O’Shea;Prayush Kumar;C. Moore
Applications of physics informed neural operators
物理知识神经算子的应用
- DOI:10.1088/2632-2153/acd168
- 发表时间:2023
- 期刊:
- 影响因子:0
- 作者:Rosofsky, Shawn G.;Al Majed, Hani;Huerta, E. A.
- 通讯作者:Huerta, E. A.
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Volodymyr Kindratenko其他文献
Enabling real-time multi-messenger astrophysics discoveries with deep learning
利用深度学习实现实时多信使天体物理学发现
- DOI:
10.1038/s42254-019-0097-4 - 发表时间:
2019-10-03 - 期刊:
- 影响因子:39.500
- 作者:
E. A. Huerta;Gabrielle Allen;Igor Andreoni;Javier M. Antelis;Etienne Bachelet;G. Bruce Berriman;Federica B. Bianco;Rahul Biswas;Matias Carrasco Kind;Kyle Chard;Minsik Cho;Philip S. Cowperthwaite;Zachariah B. Etienne;Maya Fishbach;Francisco Forster;Daniel George;Tom Gibbs;Matthew Graham;William Gropp;Robert Gruendl;Anushri Gupta;Roland Haas;Sarah Habib;Elise Jennings;Margaret W. G. Johnson;Erik Katsavounidis;Daniel S. Katz;Asad Khan;Volodymyr Kindratenko;William T. C. Kramer;Xin Liu;Ashish Mahabal;Zsuzsa Marka;Kenton McHenry;J. M. Miller;Claudia Moreno;M. S. Neubauer;Steve Oberlin;Alexander R. Olivas;Donald Petravick;Adam Rebei;Shawn Rosofsky;Milton Ruiz;Aaron Saxton;Bernard F. Schutz;Alex Schwing;Ed Seidel;Stuart L. Shapiro;Hongyu Shen;Yue Shen;Leo P. Singer;Brigitta M. Sipocz;Lunan Sun;John Towns;Antonios Tsokaros;Wei Wei;Jack Wells;Timothy J. Williams;Jinjun Xiong;Zhizhen Zhao - 通讯作者:
Zhizhen Zhao
INSTRAS: INfrared Spectroscopic imaging-based TRAnsformers for medical image Segmentation
- DOI:
10.1016/j.mlwa.2024.100549 - 发表时间:
2024-06-01 - 期刊:
- 影响因子:
- 作者:
Hangzheng Lin;Kianoush Falahkheirkhah;Volodymyr Kindratenko;Rohit Bhargava - 通讯作者:
Rohit Bhargava
Volodymyr Kindratenko的其他文献
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{{ truncateString('Volodymyr Kindratenko', 18)}}的其他基金
Collaborative Research: Frameworks: hpcGPT: Enhancing Computing Center User Support with HPC-enriched Generative AI
协作研究:框架:hpcGPT:通过 HPC 丰富的生成式 AI 增强计算中心用户支持
- 批准号:
2411295 - 财政年份:2024
- 资助金额:
$ 65.13万 - 项目类别:
Standard Grant
Collaborative Research: Frameworks: Diamond: Democratizing Large Neural Network Model Training for Science
合作研究:框架:钻石:科学大型神经网络模型训练的民主化
- 批准号:
2311768 - 财政年份:2023
- 资助金额:
$ 65.13万 - 项目类别:
Standard Grant
REU Site: The future of discovery: training students to build and apply open source machine learning models and tools
REU 网站:发现的未来:培训学生构建和应用开源机器学习模型和工具
- 批准号:
2050195 - 财政年份:2021
- 资助金额:
$ 65.13万 - 项目类别:
Standard Grant
SGER: Investigating Application Analysis and Design Methodologies for Computational Accelerators
SGER:研究计算加速器的应用分析和设计方法
- 批准号:
0810563 - 财政年份:2008
- 资助金额:
$ 65.13万 - 项目类别:
Standard Grant
Geoscience Applications on Petascale Systems: Requirements Workshops; Early in August-2005 for a 4-6 Weeks Period
Petascale 系统上的地球科学应用:需求研讨会;
- 批准号:
0540688 - 财政年份:2005
- 资助金额:
$ 65.13万 - 项目类别:
Standard Grant
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- 批准号:10774081
- 批准年份:2007
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- 项目类别:面上项目
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