Kalman filter tracking on parallel architectures Article Swipe
G. B. Cerati
,
P. Elmer
,
S. Krutelyov
,
S. Lantz
,
M. Lefebvre
,
K. Mcdermott
,
D. Riley
,
M. Tadel
,
P. Wittich
,
Frank Würthwein
,
A. Yagil
·
YOU?
·
· 2017
· Open Access
·
· DOI: https://doi.org/10.1088/1742-6596/898/4/042051
YOU?
·
· 2017
· Open Access
·
· DOI: https://doi.org/10.1088/1742-6596/898/4/042051
\nPower density constraints are limiting the performance improvements of modern CPUs. To address this we have seen the introduction of lower-power, multi-core processors such as GPGPU, ARM and Intel MIC. In order to achieve the theoretical performance gains of these processors, it will be necessary to parallelize algorithms to exploit larger numbers of lightweight cores and specialized functions like large vector units. Track finding and fitting is one of the most computationally challenging problems for event reconstruction in particle physics. At the High-Luminosity Large Hadron Collider (HL-LHC), for example, this will be by far the dominant problem. The need for greater parallelism has driven investigations of very different track finding techniques such as Cellular Automata or Hough Transforms. The most common track finding techniques in use today, however, are those based on a Kalman filter approach. Significant experience has been accumulated with these techniques on real tracking detector systems, both in the trigger and offline. They are known to provide high physics performance, are robust, and are in use today at the LHC. Given the utility of the Kalman filter in track finding, we have begun to port these algorithms to parallel architectures, namely Intel Xeon and Xeon Phi. We report here on our progress towards an end-to-end track reconstruction algorithm fully exploiting vectorization and parallelization techniques in a simplified experimental environment.\n
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1088/1742-6596/898/4/042051
- https://iopscience.iop.org/article/10.1088/1742-6596/898/4/042051/pdf
- OA Status
- diamond
- Cited By
- 7
- References
- 6
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W1677691940
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Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W1677691940Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1088/1742-6596/898/4/042051Digital Object Identifier
- Title
-
Kalman filter tracking on parallel architecturesWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2017Year of publication
- Publication date
-
2017-10-01Full publication date if available
- Authors
-
G. B. Cerati, P. Elmer, S. Krutelyov, S. Lantz, M. Lefebvre, K. Mcdermott, D. Riley, M. Tadel, P. Wittich, Frank Würthwein, A. YagilList of authors in order
- Landing page
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https://doi.org/10.1088/1742-6596/898/4/042051Publisher landing page
- PDF URL
-
https://iopscience.iop.org/article/10.1088/1742-6596/898/4/042051/pdfDirect link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
diamondOpen access status per OpenAlex
- OA URL
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https://iopscience.iop.org/article/10.1088/1742-6596/898/4/042051/pdfDirect OA link when available
- Concepts
-
SIMD, Computer science, Multithreading, Xeon Phi, Parallel computing, Exploit, Vectorization (mathematics), Instruction set, Kalman filter, Xeon, Software, Set (abstract data type), Computer architecture, Thread (computing), Artificial intelligence, Programming language, Computer securityTop concepts (fields/topics) attached by OpenAlex
- Cited by
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7Total citation count in OpenAlex
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2024: 2, 2022: 1, 2020: 2, 2018: 1, 2017: 1Per-year citation counts (last 5 years)
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6Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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