Kalman Filter Tracking on Parallel Architectures

21st International Conference on Computing in High Energy and Nuclear Physics

物理学;计算机科学

Cerati, Giuseppe^1 ; Elmer, Peter^2 ; Lantz, Steven^3 ; McDermott, Kevin^4 ; Riley, Dan^4 ; Tadel, Matev^1 ; Wittich, Peter^4 ; Würthwein, Frank^1 ; Yagil, Avi^1

University of California, San Diego

CA

92093, United States^1

Department of Physics, Princeton University, Princeton

NJ

08540, United States^2

Center for Advanced Computing, Cornell University, Ithaca

NY

14853, United States^3

Laboratory of Elementary Particle Physics, Cornell University, Ithaca

NY

14853, United States^4

关键词: Event reconstruction;    Kalman Filter Tracking;    Multi-core processor;    Parallelization techniques;    Performance improvements;    Power density constraints;    Realistic simulation;    Track reconstruction;   

Others  :  https://iopscience.iop.org/article/10.1088/1742-6596/664/7/072008/pdf
DOI  :  10.1088/1742-6596/664/7/072008

学科分类：计算机科学（综合）

来源: IOP

【 摘 要 】

Power density constraints are limiting the performance improvements of modern CPUs. To address this we have seen the introduction of lower-power, multi-core processors, but the future will be even more exciting. In order to stay within the power density limits but still obtain Moore's Law performance/price gains, it will be necessary to parallelize algorithms to exploit larger numbers of lightweight cores and specialized functions like large vector units. Example technologies today include Intel's Xeon Phi and GPGPUs. Track finding and fitting is one of the most computationally challenging problems for event reconstruction in particle physics. At the High Luminosity LHC, for example, this will be by far the dominant problem. The need for greater parallelism has driven investigations of very different track finding techniques including Cellular Automata or returning to Hough Transform. The most common track finding techniques in use today are however those based on the Kalman Filter [2]. Significant experience has been accumulated with these techniques on real tracking detector systems, both in the trigger and offline. They are known to provide high physics performance, are robust and are exactly those being used today for the design of the tracking system for HL-LHC. Our previous investigations showed that, using optimized data structures, track fitting with Kalman Filter can achieve large speedup both with Intel Xeon and Xeon Phi. We report here our further progress towards an end-to-end track reconstruction algorithm fully exploiting vectorization and parallelization techniques in a realistic simulation setup.