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）的发展是主要的智力成就。通过在CERN的大型强子对撞机（LHC）发现希格斯玻色子，进一步证实了该模型的有效性。但是，标准模型留下了许多问题，包括为什么物质主导着宇宙中的反物质和暗物质的特性。大多数解释都需要新现象，我们将其称为标准模型物理（BSM），并且在CERN的LHCB实验旨在探索它。 LHC是世界上首要的高能物理粒子加速器，目前在瑞士日内瓦附近的CERN实验室运营，这是解决这些BSM问题的最重要设施之一。 LHCB实验是LHC上的四个大型实验之一，旨在详细研究包含B或C夸克的Hadron的衰变。目的是通过检查包含这些夸克的黑龙的特性来确定超出标准模型的新物理学的存在。新的物理或新力可以通过颗粒表现出来，尚待发现，它们的存在会改变腐烂的速率和违反包含B和C夸克的HADRON的CP，从而使新现象可与新现象观察到新现象，或直接观察到新的力荷里粒子。 LHC实验收集的数据集是世界上最大的数据集。例如，PI都参与的LHCB实验的传感器阵列可产生约100 tb/s，每年接近Zettabyte。即使在通过定制读取电子设备进行大幅度的数据减少之后，数据量仍约为每年10个exabytes。如此大的数据集不能无限期地存储；因此，所有高能量物理（HEP）实验采用第二种数据还原方案，该方案由数据 - 开发系统实时执行（称为HEP中的触发系统），以决定是否要持续进行将来的分析或永久丢弃。该项目的主要目标是开发和部署软件，以最大程度地提高LHCB触发系统的性能 - 在GPU上运行其第一个处理阶段 - 以便实现LHCB的完整物理发现潜力。LHCB检测器正在运行3（在2022年开始记录触发器系统），需要Precigne System Preceens 25 Exabyty Exabyte Exabyte Exabyty exabyt precaby precaby the the the the the the the the the the the the the the the the the the the the the the the 3（启动数据都将开始记录数据）。目前，使用高级计算算法分析了每年通过触发器处理的10个exabytes中的0.3。其余的使用在FPGA上执行的简单算法在此阶段之前丢弃。为了显着扩展其在Run 3中的物理范围，LHCB计划使用高级计算算法每年处理整个25个Exabytes。 PIS建议在GPU上运行整个第一个触发阶段，该阶段的净成本为零（可能为负），并在第二处理阶段释放了所有CPU资源。 LHCB触发器大量使用机器学习（ML）算法，这将需要在运行3个条件以及GPU上的使用情况下重新优化这两个算法。该提案的具体目标正在发展：基于GPU的主要触发选择算法的版本，这些版本大量使用ML；基于GPU的量热计簇和电子识别算法，可能使用ML；以及在基于GPU的触发框架中部署ML算法所需的基础架构。这些进步将使探索暗物质的许多潜在解释，例如暗光子衰减，以及我们宇宙的物质/反物质不对称，否则这些数据否则由于触发系统的限制而无法访问，这是由于触发系统的限制而无法访问的。该奖项反映了NSF的法定任务，并通过评估了基金会的范围，并通过评估了基金会的范围，并经过评估。

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