Reconstruction of Charged Particle Tracks in Realistic Detector Geometry Using a Vectorized and Parallelized Kalman Filter Algorithm Article Swipe
G. B. Cerati
,
P. Elmer
,
Brian Gravelle
,
M. J. Kortelainen
,
V. Krutelyov
,
Steven Lantz
,
Mario Masciovecchio
,
K. Mcdermott
,
Boyana Norris
,
Allison Reinsvold Hall
,
M. Reid
,
Daniel Riley
,
M. Tadel
,
P. Wittich
,
Bei Wang
,
Frank Würthwein
,
A. Yagil
·
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.5281/zenodo.3599128
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.5281/zenodo.3599128
One of the most computationally challenging problems expected for the High-Luminosity Large Hadron Collider (HL-LHC) is finding and fitting particle tracks during event reconstruction. Algorithms used at the LHC today rely on Kalman filtering, which builds physical trajectories incrementally while incorporating material effects and error estimation. Recognizing the need for faster computational throughput, we have adapted Kalman-filter-based methods for highly parallel, many-core SIMD and SIMT architectures that are now prevalent in high-performance hardware. Previously we observed significant parallel speedups, with physics performance comparable to CMS standard tracking, on Intel Xeon, Intel Xeon Phi, and (to a limited extent) NVIDIA GPUs. While early tests were based on artificial events occurring inside an idealized barrel detector, we showed subsequently that our "mkFit" software builds tracks successfully from complex simulated events (including detector pileup) occurring inside a geometrically accurate representation of the CMS-2017 tracker. Here, we report on advances in both the computational and physics performance of mkFit, as well as progress toward integration with CMS production software (CMSSW). Recently we have improved the overall efficiency of the algorithm by preserving short track candidates at a relatively early stage rather than attempting to extend them over many layers. Moreover, mkFit formerly produced an excess of duplicate tracks; these are now explicitly removed in an additional processing step (note that mkFit builds all its tracks in parallel, so it cannot eliminate detector hits from further consideration as viable tracks are found). We demonstrate that with these enhancements, mkFit becomes a suitable choice for the first iteration of CMS tracking, and perhaps for later iterations as well. We plan to test this capability in the CMS High Level Trigger during Run 3 of the LHC, with an ultimate goal of using it in both the CMS HLT and offline reconstruction for the HL-LHC and Phase II CMS tracker.
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Metadata
- Type
- paratext
- Language
- en
- https://www.epj-conferences.org/10.1051/epjconf/202024502013/pdf
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4287867737
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4287867737Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.5281/zenodo.3599128Digital Object Identifier
- Title
-
Reconstruction of Charged Particle Tracks in Realistic Detector Geometry Using a Vectorized and Parallelized Kalman Filter AlgorithmWork title
- Type
-
paratextOpenAlex work type
- Language
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enPrimary language
- Publication year
-
2019Year of publication
- Publication date
-
2019-11-07Full publication date if available
- Authors
-
G. B. Cerati, P. Elmer, Brian Gravelle, M. J. Kortelainen, V. Krutelyov, Steven Lantz, Mario Masciovecchio, K. Mcdermott, Boyana Norris, Allison Reinsvold Hall, M. Reid, Daniel Riley, M. Tadel, P. Wittich, Bei Wang, Frank Würthwein, A. YagilList of authors in order
- PDF URL
-
https://www.epj-conferences.org/10.1051/epjconf/202024502013/pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://www.epj-conferences.org/10.1051/epjconf/202024502013/pdfDirect OA link when available
- Concepts
-
Kalman filter, Detector, Computer science, Particle filter, Algorithm, Fast Kalman filter, Extended Kalman filter, Computer vision, Computer graphics (images), Geometry, Artificial intelligence, Mathematics, TelecommunicationsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- Related works (count)
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10Other works algorithmically related by OpenAlex
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