S2I2: Institute for Research and Innovation in Software for High Energy Physics (IRIS-HEP)

S2I2：高能物理软件研究与创新研究所 (IRIS-HEP)

• 批准号: 1836650
• 负责人: G J Peter Elmer
• 金额: $ 2500万
• 依托单位: Princeton University
• 依托单位国家: 美国
• 项目类别: Cooperative Agreement
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1836650&HistoricalAwards=false
• 关键词: S2I2; Institute; Research; Innovation; Software

The quest to understand the fundamental building blocks of nature and their interactions is one of the oldest and most ambitious of human scientific endeavors. In Elementary Particles Physics, the most successful theory to date is known as the "Standard Model" of particle physics. Facilities such as CERN's Large Hadron Collider (LHC) represent a huge step forward in this quest as evidenced by the discovery of the Higgs boson. The next phase of this global scientific project will be the High-Luminosity LHC (HL-LHC), which will collect data starting circa 2026 and continue into the 2030's. The primary science goal at the HL-LHC is to search for physics beyond the Standard Model. In the HL-LHC era, the ATLAS and CMS experiments will record 10 times as much data from 100 times as many collisions as were used to discover the Higgs boson. As such, significant R&D advances must be achieved in the software for acquiring, managing, processing and analyzing HL-LHC data to realize the scientific potential of the upgraded accelerator and detectors and the planned physics program. In this context, the Institute for Research and Innovation in Software for High Energy Physics (IRIS-HEP) will play a leading role to meet the software and computing challenges of the HL-LHC. The Institute for Research and Innovation in Software for High Energy Physics (IRIS-HEP) addresses key elements of the international "Roadmap for HEP Software and Computing R&D for the 2020s" and implements the "Strategic Plan for a Scientific Software Innovation Institute (S2I2) for High Energy Physics" submitted to the NSF in December 2017. IRIS-HEP will advance R&D in three high-impact areas: (1) development of innovative algorithms for data reconstruction and triggering; (2) development of highly performant analysis systems that reduce `time-to-insight' and maximize the HL-LHC physics potential; and (3) development of data organization, management and access (DOMA) systems for the community's upcoming Exabyte era. IRIS-HEP will sustain investments in today's distributed high-throughput computing (DHTC) and build an integration path to deliver its R&D activities into the distributed production infrastructure. As an intellectual hub, IRIS-HEP will lead efforts to (1) build convergence research between HEP and the Cyberinfrastructure, Data Science and Computer Science communities for novel approaches to address the compelling software and computing challenges of HL-LHC era HEP experiments, (2) engage broadly with researchers and students from U.S. Universities and labs emphasizing professional development and training, and (3) sustain HEP software and underlying knowledge related to the algorithms and their implementations over the two decades required. In addition to enabling the best possible HL-LHC science, IRIS-HEP will bring together the larger Cyberinfrastructure and HEP communities to address the complex issues at the intersection of Exascale high-throughput computing and Exabyte-scale datasets in ways broadly relevant to many research domains with emerging data-intensive needs. The education and training provided by the Institute in the form of summer schools and a fellows program will contribute to a highly qualified STEM workforce as most students and even most post-docs move into the private sector taking their skills with them. This project advances the objectives of the National Strategic Computing Initiative (NSCI) and the objectives of "Harnessing the Data Revolution", one of the 10 Big Ideas for Future NSF Investments. This project is supported by the Office of Advanced Infrastructure in the Directorate for Computer and Information Science and Engineering and the Division of Physics in the Directorate for 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.

探索了解自然界的基本构成要素及其相互作用是人类最古老、最雄心勃勃的科学努力之一。在基本粒子物理学中，迄今为止最成功的理论被称为粒子物理的“标准模型”。欧洲核子研究中心的大型强子对撞机(LHC)等设施代表着这一探索向前迈出的一大步，希格斯玻色子的发现就是明证。这一全球科学计划的下一阶段将是高光度大型强子对撞机(HL-LHC)，它将从2026年开始收集数据，一直持续到2030年的S。HL-LHC的主要科学目标是探索超越标准模型的物理。在HL-LHC时代，ATLAS和CMS实验将记录的碰撞数据是发现希格斯玻色子的100倍的10倍。因此，必须在获取、管理、处理和分析HL-LHC数据的软件方面取得重大研发进展，以实现升级后的加速器和探测器以及计划中的物理计划的科学潜力。在这方面，高能物理软件研究与创新研究所(IRIS-HEP)将在迎接HL-LHC的软件和计算挑战方面发挥主导作用。高能物理软件研究与创新研究所(IRIS-HEP)致力于解决国际上“高能物理软件与计算研发路线图”的关键要素，并实施于2017年12月提交给美国国家科学基金会的“高能物理科学软件创新研究所(S2I2)战略规划”。IRIS-HEP将推动三个高影响力领域的研发：(1)开发用于数据重建和触发的创新算法；(2)开发高性能分析系统，以减少“洞察时间”并最大限度地发挥HL-LHC的物理潜力；以及(3)为社区即将到来的艾字节时代开发数据组织、管理和访问(DOMA)系统。IRIS-HEP将继续对当今的分布式高吞吐量计算(DHTC)进行投资，并建立一条集成路径，将其研发活动交付到分布式生产基础设施中。作为一个知识中心，IRIS-HEP将领导以下工作：(1)在HEP与网络基础设施、数据科学和计算机科学界之间建立融合研究，以寻找新的方法来解决HL-LHC时代HEP实验中引人注目的软件和计算挑战，(2)与强调专业发展和培训的美国大学和实验室的研究人员和学生广泛接触，以及(3)在所需的二十年内维持HEP软件和与算法及其实施相关的基础知识。除了实现最好的HL-LHC科学之外，IRIS-HEP还将把更大的网络基础设施和HEP社区聚集在一起，以解决亿级高通量计算和艾字节规模数据集交汇处的复杂问题，其方式与许多具有新的数据密集型需求的研究领域广泛相关。该研究所以暑期学校和研究员计划的形式提供的教育和培训将有助于培养一支高素质的STEM劳动力队伍，因为大多数学生甚至大多数博士后都会带着他们的技能进入私营部门。该项目推进了国家战略计算倡议(NSCI)的目标和“利用数据革命”的目标，这是未来NSF投资的十大想法之一。这个项目得到了计算机和信息科学与工程局的高级基础设施办公室和数学和物理科学局的物理部的支持。这个奖项反映了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

pyhf: pure-Python implementation of HistFactory statistical models

• DOI: 10.21105/joss.02823
• 发表时间: 2021-02
• 期刊: J. Open Source Softw.
• 作者: L. Heinrich;M. Feickert;G. Stark;K. Cranmer

An array-oriented Python interface for FastJet

FastJet 的面向数组的 Python 接口

• DOI: 10.1088/1742-6596/2438/1/012011
• 发表时间: 2023
• 期刊: Journal of Physics: Conference Series
• 作者: Roy, Aryan;Pivarski, Jim;Freer, Chad Wells
• 通讯作者: Freer, Chad Wells

Towards Physical Design Management in Storage Systems

存储系统中的物理设计管理

• DOI: 10.1109/pdsw49588.2019.00009
• 发表时间: 2019
• 期刊: 2019 IEEE/ACM Fourth International Parallel Data Systems Workshop (PDSW
• 作者: Dahlgren, Kathryn;LeFevre, Jeff;Shirwadkar, Ashay;Iizawa, Ken;Montana, Aldrin;Alvaro, Peter;Maltzahn, Carlos
• 通讯作者: Maltzahn, Carlos

Building and steering binned template fits with cabinetry

建筑和转向箱模板适合橱柜

• DOI: 10.1051/epjconf/202125103067
• 发表时间: 2021
• 期刊: EPJ Web of Conferences
• 作者: Cranmer, Kyle;Held, Alexander
• 通讯作者: Held, Alexander

Analyzing Transatlantic Network Traffic over Scientific Data Caches

通过科学数据缓存分析跨大西洋网络流量

• DOI: 10.1145/3589012.3594897
• 发表时间: 2023
• 期刊: Proceedings of the 2023 on Systems and Network Telemetry and Analytics
• 作者: Deng, Ziyue;Sim, Alex;Wu, Kesheng;Guok, Chin;Hazen, Damian;Monga, Inder;Andrijauskas, Fabio;Würthwein, Frank;Weitzel, Derek
• 通讯作者: Weitzel, Derek