Building particle tracks is the most computationally intense step of event reconstruction at the LHC. With the increased instantaneous luminosity and associated increase in pileup expected from the High-Luminosity LHC, the computational challenge of track finding and fitting requires novel solutions. The current track reconstruction algorithms used at the LHC are based on Kalman filter methods that achieve good physics performance. By adapting the Kalman filter techniques for use on many-core SIMD architectures such as the Intel Xeon and Intel Xeon Phi and (to a limited degree) NVIDIA GPUs, we are able to obtain significant speedups and comparable physics performance. New optimizations, including a dedicated post-processing step to remove duplicate tracks, have improved the algorithm's performance even further. Here we report on the current structure and performance of the code and future plans for the algorithm.
@article{arxiv.1906.11744,
title = {Speeding up Particle Track Reconstruction in the CMS Detector using a Vectorized and Parallelized Kalman Filter Algorithm},
author = {Giuseppe Cerati and Peter Elmer and Brian Gravelle and Matti Kortelainen and Vyacheslav Krutelyov and Steven Lantz and Mario Masciovecchio and Kevin McDermott and Boyana Norris and Michael Reid and Allison Reinsvold Hall and Daniel Riley and Matevž Tadel and Peter Wittich and Frank Würthwein and Avi Yagil},
journal= {arXiv preprint arXiv:1906.11744},
year = {2019}
}
Comments
Submitted to proceedings of the 2019 Connecting the Dots and Workshop on Intelligent Trackers (CTD/WIT 2019); 6 pages, 4 figures