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Title: Speeding up Particle Track Reconstruction in the CMS Detector using a Vectorized and Parallelized Kalman Filter Algorithm

Abstract

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.

Authors:
ORCiD logo [1];  [2];  [3]; ORCiD logo [1];  [4];  [5];  [4];  [5];  [3];  [1];  [5];  [4];  [5];  [4];  [4]
  1. Fermilab
  2. Princeton U.
  3. Oregon U.
  4. UC, San Diego
  5. Cornell U.
Publication Date:
Research Org.:
Fermi National Accelerator Lab. (FNAL), Batavia, IL (United States)
Sponsoring Org.:
USDOE Office of Science (SC), High Energy Physics (HEP) (SC-25)
OSTI Identifier:
1565954
Report Number(s):
arXiv:1906.11744; FERMILAB-CONF-19-387-CD
oai:inspirehep.net:1741870
DOE Contract Number:  
AC02-07CH11359
Resource Type:
Conference
Resource Relation:
Conference: Connecting the Dots and Workshop on Intelligent Trackers, Valencia, Valencia, Spain, 04/02-04/05/2019
Country of Publication:
United States
Language:
English
Subject:
46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND TECHNOLOGY; 72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS

Citation Formats

Cerati, G., Elmer, P., Gravelle, B., Kortelainen, M., Krutelyov, V., Lantz, S., Masciovecchio, M., McDermott, Kevin, Norris, B., Reinsvold Hall, A., Riley, Daniel, Tadel, M., Wittich, P., Würthwein, F., and Yagil, A. Speeding up Particle Track Reconstruction in the CMS Detector using a Vectorized and Parallelized Kalman Filter Algorithm. United States: N. p., 2019. Web.
Cerati, G., Elmer, P., Gravelle, B., Kortelainen, M., Krutelyov, V., Lantz, S., Masciovecchio, M., McDermott, Kevin, Norris, B., Reinsvold Hall, A., Riley, Daniel, Tadel, M., Wittich, P., Würthwein, F., & Yagil, A. Speeding up Particle Track Reconstruction in the CMS Detector using a Vectorized and Parallelized Kalman Filter Algorithm. United States.
Cerati, G., Elmer, P., Gravelle, B., Kortelainen, M., Krutelyov, V., Lantz, S., Masciovecchio, M., McDermott, Kevin, Norris, B., Reinsvold Hall, A., Riley, Daniel, Tadel, M., Wittich, P., Würthwein, F., and Yagil, A. Thu . "Speeding up Particle Track Reconstruction in the CMS Detector using a Vectorized and Parallelized Kalman Filter Algorithm". United States. https://www.osti.gov/servlets/purl/1565954.
@article{osti_1565954,
title = {Speeding up Particle Track Reconstruction in the CMS Detector using a Vectorized and Parallelized Kalman Filter Algorithm},
author = {Cerati, G. and Elmer, P. and Gravelle, B. and Kortelainen, M. and Krutelyov, V. and Lantz, S. and Masciovecchio, M. and McDermott, Kevin and Norris, B. and Reinsvold Hall, A. and Riley, Daniel and Tadel, M. and Wittich, P. and Würthwein, F. and Yagil, A.},
abstractNote = {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.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2019},
month = {6}
}

Conference:
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