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
,
M. Reid
,
Allison Reinsvold Hall
,
Daniel Riley
,
M. Tadel
,
P. Wittich
,
Bei Wang
,
Frank Würthwein
,
A. Yagil
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1051/epjconf/202024502013
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1051/epjconf/202024502013
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-filterbased 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. 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. We demonstrate that with these enhancements, mkFit becomes a suitable choice for the first iteration of CMS tracking, and eventually 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 CMS tracker.
Related Topics
Concepts
SIMD
Xeon Phi
Large Hadron Collider
Xeon
Kalman filter
Algorithm
Computer science
Detector
Software
Event reconstruction
Tracking (education)
Parallel computing
Event (particle physics)
Computational science
Artificial intelligence
Physics
Particle physics
Programming language
Quantum mechanics
Telecommunications
Psychology
Pedagogy
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1051/epjconf/202024502013
- https://www.epj-conferences.org/articles/epjconf/pdf/2020/21/epjconf_chep2020_02013.pdf
- OA Status
- diamond
- Cited By
- 2
- References
- 10
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3005680750
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W3005680750Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1051/epjconf/202024502013Digital Object Identifier
- Title
-
Reconstruction of Charged Particle Tracks in Realistic Detector Geometry Using a Vectorized and Parallelized Kalman Filter AlgorithmWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2020Year of publication
- Publication date
-
2020-01-01Full 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, M. Reid, Allison Reinsvold Hall, Daniel Riley, M. Tadel, P. Wittich, Bei Wang, Frank Würthwein, A. YagilList of authors in order
- Landing page
-
https://doi.org/10.1051/epjconf/202024502013Publisher landing page
- PDF URL
-
https://www.epj-conferences.org/articles/epjconf/pdf/2020/21/epjconf_chep2020_02013.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
diamondOpen access status per OpenAlex
- OA URL
-
https://www.epj-conferences.org/articles/epjconf/pdf/2020/21/epjconf_chep2020_02013.pdfDirect OA link when available
- Concepts
-
SIMD, Xeon Phi, Large Hadron Collider, Xeon, Kalman filter, Algorithm, Computer science, Detector, Software, Event reconstruction, Tracking (education), Parallel computing, Event (particle physics), Computational science, Artificial intelligence, Physics, Particle physics, Programming language, Quantum mechanics, Telecommunications, Psychology, PedagogyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
2Total citation count in OpenAlex
- Citations by year (recent)
-
2021: 2Per-year citation counts (last 5 years)
- References (count)
-
10Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.showed | 115 |
| abstract_inverted_index.toward | 159 |
| abstract_inverted_index.tracks | 20, 122 |
| abstract_inverted_index.Trigger | 249 |
| abstract_inverted_index.adapted | 55 |
| abstract_inverted_index.becomes | 220 |
| abstract_inverted_index.complex | 125 |
| abstract_inverted_index.effects | 42 |
| abstract_inverted_index.extent) | 97 |
| abstract_inverted_index.finding | 16 |
| abstract_inverted_index.fitting | 18 |
| abstract_inverted_index.layers. | 193 |
| abstract_inverted_index.limited | 96 |
| abstract_inverted_index.methods | 57 |
| abstract_inverted_index.offline | 269 |
| abstract_inverted_index.overall | 170 |
| abstract_inverted_index.physics | 80, 151 |
| abstract_inverted_index.pileup) | 130 |
| abstract_inverted_index.removed | 207 |
| abstract_inverted_index.tracks; | 202 |
| abstract_inverted_index.(HL-LHC) | 14 |
| abstract_inverted_index.CMS-2017 | 139 |
| abstract_inverted_index.Collider | 13 |
| abstract_inverted_index.Recently | 165 |
| abstract_inverted_index.accurate | 135 |
| abstract_inverted_index.advances | 145 |
| abstract_inverted_index.detector | 129 |
| abstract_inverted_index.expected | 7 |
| abstract_inverted_index.formerly | 196 |
| abstract_inverted_index.improved | 168 |
| abstract_inverted_index.material | 41 |
| abstract_inverted_index.observed | 75 |
| abstract_inverted_index.parallel | 77 |
| abstract_inverted_index.particle | 19 |
| abstract_inverted_index.physical | 36 |
| abstract_inverted_index.problems | 6 |
| abstract_inverted_index.produced | 197 |
| abstract_inverted_index.progress | 158 |
| abstract_inverted_index.software | 120 |
| abstract_inverted_index.standard | 85 |
| abstract_inverted_index.suitable | 222 |
| abstract_inverted_index.tracker. | 140, 275 |
| abstract_inverted_index.ultimate | 258 |
| abstract_inverted_index.Moreover, | 194 |
| abstract_inverted_index.algorithm | 174 |
| abstract_inverted_index.detector, | 113 |
| abstract_inverted_index.duplicate | 201 |
| abstract_inverted_index.hardware. | 72 |
| abstract_inverted_index.idealized | 111 |
| abstract_inverted_index.iteration | 227 |
| abstract_inverted_index.many-core | 61 |
| abstract_inverted_index.occurring | 108, 131 |
| abstract_inverted_index.parallel, | 60 |
| abstract_inverted_index.prevalent | 69 |
| abstract_inverted_index.simulated | 126 |
| abstract_inverted_index.software. | 164 |
| abstract_inverted_index.speedups, | 78 |
| abstract_inverted_index.tracking, | 86, 230 |
| abstract_inverted_index.(including | 128 |
| abstract_inverted_index.Algorithms | 24 |
| abstract_inverted_index.Previously | 73 |
| abstract_inverted_index.additional | 210 |
| abstract_inverted_index.artificial | 106 |
| abstract_inverted_index.attempting | 187 |
| abstract_inverted_index.candidates | 179 |
| abstract_inverted_index.capability | 243 |
| abstract_inverted_index.comparable | 82 |
| abstract_inverted_index.efficiency | 171 |
| abstract_inverted_index.eventually | 232 |
| abstract_inverted_index.explicitly | 206 |
| abstract_inverted_index.filtering, | 33 |
| abstract_inverted_index.iterations | 235 |
| abstract_inverted_index.preserving | 176 |
| abstract_inverted_index.processing | 211 |
| abstract_inverted_index.production | 163 |
| abstract_inverted_index.relatively | 182 |
| abstract_inverted_index.Recognizing | 46 |
| abstract_inverted_index.challenging | 5 |
| abstract_inverted_index.demonstrate | 214 |
| abstract_inverted_index.estimation. | 45 |
| abstract_inverted_index.integration | 160 |
| abstract_inverted_index.performance | 81, 152 |
| abstract_inverted_index.significant | 76 |
| abstract_inverted_index.throughput, | 52 |
| abstract_inverted_index.subsequently | 116 |
| abstract_inverted_index.successfully | 123 |
| abstract_inverted_index.trajectories | 37 |
| abstract_inverted_index.architectures | 65 |
| abstract_inverted_index.computational | 51, 149 |
| abstract_inverted_index.enhancements, | 218 |
| abstract_inverted_index.geometrically | 134 |
| abstract_inverted_index.incorporating | 40 |
| abstract_inverted_index.incrementally | 38 |
| abstract_inverted_index.reconstruction | 270 |
| abstract_inverted_index.representation | 136 |
| abstract_inverted_index.High-Luminosity | 10 |
| abstract_inverted_index.computationally | 4 |
| abstract_inverted_index.reconstruction. | 23 |
| abstract_inverted_index.high-performance | 71 |
| abstract_inverted_index.Kalman-filterbased | 56 |
| cited_by_percentile_year.max | 95 |
| cited_by_percentile_year.min | 93 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 17 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/9 |
| sustainable_development_goals[0].score | 0.44999998807907104 |
| sustainable_development_goals[0].display_name | Industry, innovation and infrastructure |
| citation_normalized_percentile.value | 0.85735238 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |