Collaborative Research: Particle Tracking at High Luminosity on Heterogeneous, Parallel Processor Architectures

协作研究：异构并行处理器架构上的高亮度粒子跟踪

• 批准号: 1521042
• 负责人: G J Peter Elmer
• 金额: $ 47.95万
• 依托单位: Princeton University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-08-01 至 2020-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1521042&HistoricalAwards=false
• 关键词: Collaborative; Research; Particle; Tracking; Luminosity

Particle physics experiments at the Large Hadron Collider (LHC) at CERN seek to explore fundamental questions in modern physics, such as how particles attain mass, why gravity is weak, and the nature of dark matter. The large quantity of data produced at experimental facilities such as the LHC requires the development of complex pattern recognition algorithms and software techniques to achieve the scientific goals of these physics programs. This project will investigate new algorithms and techniques for data analysis using emerging computing processor architectures. These activities will enable the LHC experiments to take data more efficiently and improve the quality of the data that is recorded in order to extend the reach of the next generation of discoveries from planned hardware upgrades at the LHC over the next decade. The results of this research will significantly reduce the cost of computing for all LHC experiments. Software source code tools will be made available to the particle physics community. The investigators will host workshops to train post-doctoral fellows and graduate students from all areas of particle physics on how to use these advanced techniques. This training is valuable preparation for dealing with big data science in general.This project will support research focused on novel compute architectures for parallelized and vectorized charged particle track reconstruction. This research will improve the reach of energy-frontier particle physics experiments, such as the ATLAS, CMS, LHCb and ALICE experiments at the LHC and any other fields where studying the passage of charged particles is of critical importance. The science targeted in this project includes studying the properties of the Higgs boson, probing dark matter by searching for supersymmetry, and exploring the unknown by looking for such proposed effects as large extra space-time dimensions.

在欧洲核子研究中心（CERN）的大型强子对撞机（LHC）进行的粒子物理实验试图探索现代物理学中的基本问题，例如粒子如何获得质量，引力为什么弱，以及暗物质的本质。大型强子对撞机等实验设备产生的大量数据需要开发复杂的模式识别算法和软件技术，以实现这些物理项目的科学目标。该项目将研究使用新兴计算处理器架构进行数据分析的新算法和技术。这些活动将使大型强子对撞机实验能够更有效地获取数据，并提高所记录数据的质量，以便在未来十年扩大大型强子对撞机硬件升级计划的下一代发现的范围。这项研究的结果将显著降低所有大型强子对撞机实验的计算成本。软件源代码工具将提供给粒子物理社区。研究人员将举办研讨会，培训来自粒子物理学各个领域的博士后研究员和研究生如何使用这些先进技术。这种培训是处理大数据科学的宝贵准备。该项目将支持用于并行化和矢量化带电粒子轨迹重建的新型计算架构的研究。这项研究将提高能量前沿粒子物理实验的覆盖面，如大型强子对撞机的ATLAS、CMS、LHCb和ALICE实验，以及其他研究带电粒子通过至关重要的领域。这个项目的科学目标包括研究希格斯玻色子的性质，通过寻找超对称来探测暗物质，以及通过寻找诸如大的额外时空维度之类的假设效应来探索未知。