Collaborative Research : Elements : Extending the physics reach of LHCb by developing and deploying algorithms for a fully GPU-based first trigger stage

合作研究：要素：通过开发和部署完全基于 GPU 的第一触发阶段的算法来扩展 LHCb 的物理范围

• 批准号: 2004645
• 负责人: Mike Williams
• 金额: $ 31万
• 依托单位: Massachusetts Institute of Technology
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-07-01 至 2024-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2004645&HistoricalAwards=false
• 关键词: Collaborative; Research; Elements; Extending; physics

The development of the Standard Model (SM) of particle physics is a major intellectual achievement. The validity of this model was further confirmed by the discovery of the Higgs boson at the Large Hadron Collider (LHC) at CERN. However, the Standard Model leaves open many questions, including why matter dominates over anti-matter in the Universe and the properties of dark matter. Most explanations require new phenomena, which we call Beyond the Standard Model Physics (BSM), and which the LHCb experiment at CERN has been designed to explore. The LHC is the premier High Energy Physics particle accelerator in the world and is currently operating at the CERN laboratory near Geneva Switzerland, one of the foremost facilities for addressing these BSM questions. The LHCb experiment is one of four large experiments at the LHC and is designed to study in detail the decays of hadrons containing b or c quarks. The goal is to identify the existence of new physics beyond the Standard Model by examining the properties of hadrons containing these quarks. The new physics, or new forces, can be manifest by particles, as yet to be discovered, whose presence would modify decay rates and CP violating asymmetries of hadrons containing the b and c quarks, allowing new phenomena to be observed indirectly - or via direct observation of new force-carrying particles. The data sets collected by the LHC experiments are some of the largest in the world. For example, the sensor arrays of the LHCb experiment, in which both PIs participate, produce about 100 TB/s and close to a zettabyte per year. Even after drastic data-reduction performed by custom-built read-out electronics, the data volume is still about 10 exabytes per year. Such large data sets cannot be stored indefinitely; therefore, all high energy physics (HEP) experiments employ a second data-reduction scheme executed in real time by a data-ingestion system - referred to as a trigger system in HEP - to decide whether each event is to be persisted for future analysis or permanently discarded. The primary goal of this project is developing and deploying software that will maximize the performance of the LHCb trigger system - running its first processing stage on GPUs - so that the full physics discovery potential of LHCb is realized.The LHCb detector is being upgraded for Run 3 (which will start to record data in 2022), when the trigger system will need to process 25 exabytes per year. Currently, only 0.3 of the 10 exabytes per year processed by the trigger is analyzed using high-level computing algorithms; the rest is discarded prior to this stage using simple algorithms executed on FPGAs. To significantly extend its physics reach in Run 3, LHCb plans to process the entire 25 exabytes each year using high-level computing algorithms. The PIs propose running the entire first trigger-processing stage on GPUs, which has zero (likely negative) net cost, and frees up all of the CPU resources for the second processing stage. The LHCb trigger makes heavy use of machine learning (ML) algorithms, which will need to be reoptimized both for Run 3 conditions but also for usage on GPUs. The specific objectives of this proposal are developing: GPU-based versions of the primary trigger-selection algorithms, which make heavy usage of ML; GPU-based calorimeter-clustering and electron-identification algorithms, likely using ML; and the infrastructure required to deploy ML algorithms within the GPU-based trigger framework. These advances will make it possible to explore many potential explanations for dark matter, e.g., dark photon decays, and the matter/anti-matter asymmetry of our universe using data that would be otherwise inaccessible due to trigger-system limitations.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.

粒子物理学标准模型（SM）的发展是一项重大的智力成就。欧洲核子研究中心大型强子对撞机（LHC）希格斯玻色子的发现进一步证实了该模型的有效性。然而，标准模型留下了许多悬而未决的问题，包括为什么物质在宇宙中比反物质占主导地位以及暗物质的性质。大多数解释都需要新的现象，我们称之为“超越标准模型物理”（BSM），CERN 的 LHCb 实验就是为了探索这一现象而设计的。 LHC 是世界上首屈一指的高能物理粒子加速器，目前在瑞士日内瓦附近的 CERN 实验室运行，该实验室是解决这些 BSM 问题的最重要的设施之一。 LHCb 实验是 LHC 的四个大型实验之一，旨在详细研究含有 b 或 c 夸克的强子的衰变。目标是通过检查包含这些夸克的强子的性质来确定标准模型之外的新物理学的存在。新物理或新力量可以通过尚未发现的粒子来体现，它们的存在将改变包含 b 和 c 夸克的强子的衰变率和 CP 破坏不对称性，从而允许间接观察到新现象 - 或通过直接观察观察新的载力粒子。大型强子对撞机实验收集的数据集是世界上最大的。例如，两位 PI 参与的 LHCb 实验的传感器阵列每年产生约 100 TB/s 的数据，接近 1 ZB。即使通过定制的读出电子设备大幅减少数据后，每年的数据量仍然约为 10 艾字节。如此大的数据集不可能无限期地存储；因此，所有高能物理 (HEP) 实验都采用由数据摄取系统（在 HEP 中称为触发系统）实时执行的第二种数据缩减方案，以决定是否保留每个事件以供将来分析或永久丢弃。该项目的主要目标是开发和部署软件，以最大限度地提高 LHCb 触发系统的性能（在 GPU 上运行其第一个处理阶段），从而实现 LHCb 的全部物理发现潜力。LHCb 探测器正在升级为运行3（将于2022年开始记录数据），届时触发系统每年需要处理25艾字节。目前，触发器每年处理的 10 艾字节中只有 0.3 个是使用高级计算算法进行分析的；在此阶段之前，使用在 FPGA 上执行的简单算法丢弃其余部分。为了在 Run 3 中显着扩展其物理范围，LHCb 计划每年使用高级计算算法处理全部 25 艾字节。 PI 建议在 GPU 上运行整个第一个触发处理阶段，其净成本为零（可能为负），并为第二个处理阶段释放所有 CPU 资源。 LHCb 触发器大量使用机器学习 (ML) 算法，需要针对 Run 3 条件以及 GPU 上的使用重新优化该算法。该提案的具体目标是开发： 主要触发选择算法的基于 GPU 的版本，该算法大量使用了 ML；基于 GPU 的热量计聚类和电子识别算法，可能使用机器学习；以及在基于 GPU 的触发框架内部署 ML 算法所需的基础设施。这些进步将使探索暗物质的许多潜在解释成为可能，例如暗光子衰变以及我们宇宙的物质/反物质不对称性，使用由于触发系统限制而无法获得的数据。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的智力优点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

Robust and provably monotonic networks

• DOI: 10.1088/2632-2153/aced80
• 发表时间: 2021-11
• 期刊: Machine Learning: Science and Technology
• 作者: O. Kitouni;N. Nolte;Mike Williams