Title: Parallelized and Vectorized Tracking Using Kalman Filters with CMS Detector Geometry and Events Show Chinese title

Title: 基于CMS探测器几何和事件的并行化和向量化跟踪卡尔曼滤波器

Abstract: The High-Luminosity Large Hadron Collider at CERN will be characterized by greater pileup of events and higher occupancy, making the track reconstruction even more computationally demanding. Existing algorithms at the LHC are based on Kalman filter techniques with proven excellent physics performance under a variety of conditions. Starting in 2014, we have been developing Kalman-filter-based methods for track finding and fitting adapted for many-core SIMD processors that are becoming dominant in high-performance systems. This paper summarizes the latest extensions to our software that allow it to run on the realistic CMS-2017 tracker geometry using CMSSW-generated events, including pileup. The reconstructed tracks can be validated against either the CMSSW simulation that generated the hits, or the CMSSW reconstruction of the tracks. In general, the code's computational performance has continued to improve while the above capabilities were being added. We demonstrate that the present Kalman filter implementation is able to reconstruct events with comparable physics performance to CMSSW, while providing generally better computational performance. Further plans for advancing the software are discussed. Show Chinese abstract

Abstract: 高亮度大型强子对撞机在欧洲核子研究中心将具有更多的事件堆积和更高的占用率，这使得轨迹重建更加计算密集。 大型强子对撞机现有的算法基于卡尔曼滤波技术，在各种条件下都表现出卓越的物理性能。 从2014年开始，我们一直在开发适用于多核SIMD处理器的基于卡尔曼滤波的方法，这些处理器在高性能系统中变得越来越主导。 本文总结了我们软件的最新扩展，使其能够在使用CMSSW生成的事件（包括堆积）的现实CMS-2017跟踪器几何上运行。 重建的轨迹可以与生成这些点的CMSSW模拟进行验证，或者与CMSSW对轨迹的重建进行验证。 一般来说，在添加上述功能的同时，代码的计算性能持续提高。 我们证明了当前的卡尔曼滤波实现能够以与CMSSW相当的物理性能重建事件，同时通常提供更好的计算性能。 讨论了进一步提升软件的计划。