Generalizing mkFit and its Application to HL-LHC
Giuseppe Cerati, Peter Elmer, Patrick Gartung, Leonardo Giannini,, Matti Kortelainen, Vyacheslav Krutelyov, Steven Lantz, Mario Masciovecchio,, Tres Reid, Allison Reinsvold Hall, Daniel Riley, Matevz Tadel, Emmanouil, Vourliotis, Peter Wittich

TL;DR
This paper discusses the mkFit algorithm's deployment in CMS for HL-LHC, highlighting its speed improvements, generalization to new geometries, and potential for broader detector applications.
Contribution
The paper introduces a generalized geometry description for mkFit, enabling support for HL-LHC tracker geometry and other configurations, with detailed implementation strategies.
Findings
Average speedup of 3.5x in track pattern recognition
Effective vectorization using icc and gcc compilers
Generalized geometry supports HL-LHC tracker and other detectors
Abstract
mkFit is an implementation of the Kalman filter-based track reconstruction algorithm that exploits both thread- and data-level parallelism. In the past few years the project transitioned from the R&D phase to deployment in the Run-3 offline workflow of the CMS experiment. The CMS tracking performs a series of iterations, targeting reconstruction of tracks of increasing difficulty after removing hits associated to tracks found in previous iterations. mkFit has been adopted for several of the tracking iterations, which contribute to the majority of reconstructed tracks. When tested in the standard conditions for production jobs, speedups in track pattern recognition are on average of the order of 3.5x for the iterations where it is used (3-7x depending on the iteration). Multiple factors contribute to the observed speedups, including vectorization and a lightweight geometry description,…
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Taxonomy
TopicsParticle Detector Development and Performance · Medical Imaging Techniques and Applications · Particle physics theoretical and experimental studies
