Segment Linking: A Highly Parallelizable Track Reconstruction Algorithm for HL-LHC Article Swipe
P. Chang
,
P. Elmer
,
Yanxi Gu
,
V. Krutelyov
,
Gavin Niendorf
,
M. Reid
,
B. V. Sathia Narayanan
,
M. Tadel
,
E. Vourliotis
,
Bei Wang
,
P. Wittich
,
A. Yagil
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2209.13711
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2209.13711
The High Luminosity upgrade of the Large Hadron Collider (HL-LHC) will produce particle collisions with up to 200 simultaneous proton-proton interactions. These unprecedented conditions will create a combinatorial complexity for charged-particle track reconstruction that demands a computational cost that is expected to surpass the projected computing budget using conventional CPUs. Motivated by this and taking into account the prevalence of heterogeneous computing in cutting-edge High Performance Computing centers, we propose an efficient, fast and highly parallelizable bottom-up approach to track reconstruction for the HL-LHC, along with an associated implementation on GPUs, in the context of the Phase 2 CMS outer tracker. Our algorithm, called Segment Linking (or Line Segment Tracking), takes advantage of localized track stub creation, combining individual stubs to progressively form higher level objects that are subject to kinematical and geometrical requirements compatible with genuine physics tracks. The local nature of the algorithm makes it ideal for parallelization under the Single Instruction, Multiple Data paradigm, as hundreds of objects can be built simultaneously. The computing and physics performance of the algorithm has been tested on an NVIDIA Tesla V100 GPU, already yielding efficiency and timing measurements that are on par with the latest, multi-CPU versions of existing CMS tracking algorithms.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2209.13711
- https://arxiv.org/pdf/2209.13711
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
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All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W4298051444Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2209.13711Digital Object Identifier
- Title
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Segment Linking: A Highly Parallelizable Track Reconstruction Algorithm for HL-LHCWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2022Year of publication
- Publication date
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2022-09-27Full publication date if available
- Authors
-
P. Chang, P. Elmer, Yanxi Gu, V. Krutelyov, Gavin Niendorf, M. Reid, B. V. Sathia Narayanan, M. Tadel, E. Vourliotis, Bei Wang, P. Wittich, A. YagilList of authors in order
- Landing page
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https://arxiv.org/abs/2209.13711Publisher landing page
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https://arxiv.org/pdf/2209.13711Direct link to full text PDF
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YesWhether a free full text is available
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greenOpen access status per OpenAlex
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https://arxiv.org/pdf/2209.13711Direct OA link when available
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Large Hadron Collider, Parallelizable manifold, Computer science, Track (disk drive), Upgrade, Tracking (education), Context (archaeology), Algorithm, Parallel computing, Computational science, Particle physics, Physics, Operating system, Biology, Pedagogy, Paleontology, PsychologyTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.GPU, | 181 |
| abstract_inverted_index.High | 1, 64 |
| abstract_inverted_index.Line | 107 |
| abstract_inverted_index.V100 | 180 |
| abstract_inverted_index.been | 174 |
| abstract_inverted_index.cost | 37 |
| abstract_inverted_index.fast | 72 |
| abstract_inverted_index.form | 122 |
| abstract_inverted_index.into | 55 |
| abstract_inverted_index.stub | 115 |
| abstract_inverted_index.that | 33, 38, 126, 188 |
| abstract_inverted_index.this | 52 |
| abstract_inverted_index.will | 10, 24 |
| abstract_inverted_index.with | 14, 85, 135, 192 |
| abstract_inverted_index.CPUs. | 49 |
| abstract_inverted_index.GPUs, | 90 |
| abstract_inverted_index.Large | 6 |
| abstract_inverted_index.Phase | 96 |
| abstract_inverted_index.Tesla | 179 |
| abstract_inverted_index.These | 21 |
| abstract_inverted_index.along | 84 |
| abstract_inverted_index.built | 163 |
| abstract_inverted_index.ideal | 147 |
| abstract_inverted_index.level | 124 |
| abstract_inverted_index.local | 140 |
| abstract_inverted_index.makes | 145 |
| abstract_inverted_index.outer | 99 |
| abstract_inverted_index.stubs | 119 |
| abstract_inverted_index.takes | 110 |
| abstract_inverted_index.track | 31, 79, 114 |
| abstract_inverted_index.under | 150 |
| abstract_inverted_index.using | 47 |
| abstract_inverted_index.Hadron | 7 |
| abstract_inverted_index.NVIDIA | 178 |
| abstract_inverted_index.Single | 152 |
| abstract_inverted_index.budget | 46 |
| abstract_inverted_index.called | 103 |
| abstract_inverted_index.create | 25 |
| abstract_inverted_index.higher | 123 |
| abstract_inverted_index.highly | 74 |
| abstract_inverted_index.nature | 141 |
| abstract_inverted_index.taking | 54 |
| abstract_inverted_index.tested | 175 |
| abstract_inverted_index.timing | 186 |
| abstract_inverted_index.HL-LHC, | 83 |
| abstract_inverted_index.Linking | 105 |
| abstract_inverted_index.Segment | 104, 108 |
| abstract_inverted_index.account | 56 |
| abstract_inverted_index.already | 182 |
| abstract_inverted_index.context | 93 |
| abstract_inverted_index.demands | 34 |
| abstract_inverted_index.genuine | 136 |
| abstract_inverted_index.latest, | 194 |
| abstract_inverted_index.objects | 125, 160 |
| abstract_inverted_index.physics | 137, 168 |
| abstract_inverted_index.produce | 11 |
| abstract_inverted_index.propose | 69 |
| abstract_inverted_index.subject | 128 |
| abstract_inverted_index.surpass | 42 |
| abstract_inverted_index.tracks. | 138 |
| abstract_inverted_index.upgrade | 3 |
| abstract_inverted_index.(HL-LHC) | 9 |
| abstract_inverted_index.Collider | 8 |
| abstract_inverted_index.Multiple | 154 |
| abstract_inverted_index.approach | 77 |
| abstract_inverted_index.centers, | 67 |
| abstract_inverted_index.existing | 198 |
| abstract_inverted_index.expected | 40 |
| abstract_inverted_index.hundreds | 158 |
| abstract_inverted_index.particle | 12 |
| abstract_inverted_index.tracker. | 100 |
| abstract_inverted_index.tracking | 200 |
| abstract_inverted_index.versions | 196 |
| abstract_inverted_index.yielding | 183 |
| abstract_inverted_index.Computing | 66 |
| abstract_inverted_index.Motivated | 50 |
| abstract_inverted_index.advantage | 111 |
| abstract_inverted_index.algorithm | 144, 172 |
| abstract_inverted_index.bottom-up | 76 |
| abstract_inverted_index.combining | 117 |
| abstract_inverted_index.computing | 45, 61, 166 |
| abstract_inverted_index.creation, | 116 |
| abstract_inverted_index.localized | 113 |
| abstract_inverted_index.multi-CPU | 195 |
| abstract_inverted_index.paradigm, | 156 |
| abstract_inverted_index.projected | 44 |
| abstract_inverted_index.Luminosity | 2 |
| abstract_inverted_index.Tracking), | 109 |
| abstract_inverted_index.algorithm, | 102 |
| abstract_inverted_index.associated | 87 |
| abstract_inverted_index.collisions | 13 |
| abstract_inverted_index.compatible | 134 |
| abstract_inverted_index.complexity | 28 |
| abstract_inverted_index.conditions | 23 |
| abstract_inverted_index.efficiency | 184 |
| abstract_inverted_index.efficient, | 71 |
| abstract_inverted_index.individual | 118 |
| abstract_inverted_index.prevalence | 58 |
| abstract_inverted_index.Performance | 65 |
| abstract_inverted_index.algorithms. | 201 |
| abstract_inverted_index.geometrical | 132 |
| abstract_inverted_index.kinematical | 130 |
| abstract_inverted_index.performance | 169 |
| abstract_inverted_index.Instruction, | 153 |
| abstract_inverted_index.conventional | 48 |
| abstract_inverted_index.cutting-edge | 63 |
| abstract_inverted_index.measurements | 187 |
| abstract_inverted_index.requirements | 133 |
| abstract_inverted_index.simultaneous | 18 |
| abstract_inverted_index.combinatorial | 27 |
| abstract_inverted_index.computational | 36 |
| abstract_inverted_index.heterogeneous | 60 |
| abstract_inverted_index.interactions. | 20 |
| abstract_inverted_index.progressively | 121 |
| abstract_inverted_index.proton-proton | 19 |
| abstract_inverted_index.unprecedented | 22 |
| abstract_inverted_index.implementation | 88 |
| abstract_inverted_index.parallelizable | 75 |
| abstract_inverted_index.reconstruction | 32, 80 |
| abstract_inverted_index.parallelization | 149 |
| abstract_inverted_index.simultaneously. | 164 |
| abstract_inverted_index.charged-particle | 30 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 12 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/9 |
| sustainable_development_goals[0].score | 0.47999998927116394 |
| sustainable_development_goals[0].display_name | Industry, innovation and infrastructure |
| citation_normalized_percentile.value | 0.04153887 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |