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GPU Enhancement of the Trigger to Extend Physics Reach at the LHC

At the Large Hadron Collider (LHC), the trigger systems for the detectors must be able to process a very large amount of data in a very limited amount of time, so that the nominal collision rate of 40 MHz can be reduced to a data rate that can be stored and processed in a reasonable amount of time....

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Detalles Bibliográficos
Autores principales: Lujan, P., Halyo, V., Hunt, A., Jindal, P., LeGresley, P.
Lenguaje:eng
Publicado: 2013
Materias:
Acceso en línea:https://dx.doi.org/10.1088/1742-6596/513/1/012019
http://cds.cern.ch/record/1627715
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author Lujan, P.
Halyo, V.
Hunt, A.
Jindal, P.
LeGresley, P.
author_facet Lujan, P.
Halyo, V.
Hunt, A.
Jindal, P.
LeGresley, P.
author_sort Lujan, P.
collection CERN
description At the Large Hadron Collider (LHC), the trigger systems for the detectors must be able to process a very large amount of data in a very limited amount of time, so that the nominal collision rate of 40 MHz can be reduced to a data rate that can be stored and processed in a reasonable amount of time. This need for high performance places very stringent requirements on the complexity of the algorithms that can be used for identifying events of interest in the trigger system, which potentially limits the ability to trigger on signatures of various new physics models. In this paper, we present an alternative tracking algorithm, based on the Hough transform, which avoids many of the problems associated with the standard combinatorial track finding currently used. The Hough transform is also well-adapted for Graphics Processing Unit (GPU)-based computing, and such GPU-based systems could be easily integrated into the existing High-Level Trigger (HLT). This algorithm offers the ability to trigger on topological signatures of new physics currently not practical to reconstruct, such as events with jets or black holes significantly displaced from the primary vertex. This paper presents, for the first time, an implementation and preliminary performance results using NVIDIA Tesla C2075 and K20c GPUs.
id cern-1627715
institution Organización Europea para la Investigación Nuclear
language eng
publishDate 2013
record_format invenio
spelling cern-16277152022-08-10T20:48:41Zdoi:10.1088/1742-6596/513/1/012019http://cds.cern.ch/record/1627715engLujan, P.Halyo, V.Hunt, A.Jindal, P.LeGresley, P.GPU Enhancement of the Trigger to Extend Physics Reach at the LHCDetectors and Experimental TechniquesAt the Large Hadron Collider (LHC), the trigger systems for the detectors must be able to process a very large amount of data in a very limited amount of time, so that the nominal collision rate of 40 MHz can be reduced to a data rate that can be stored and processed in a reasonable amount of time. This need for high performance places very stringent requirements on the complexity of the algorithms that can be used for identifying events of interest in the trigger system, which potentially limits the ability to trigger on signatures of various new physics models. In this paper, we present an alternative tracking algorithm, based on the Hough transform, which avoids many of the problems associated with the standard combinatorial track finding currently used. The Hough transform is also well-adapted for Graphics Processing Unit (GPU)-based computing, and such GPU-based systems could be easily integrated into the existing High-Level Trigger (HLT). This algorithm offers the ability to trigger on topological signatures of new physics currently not practical to reconstruct, such as events with jets or black holes significantly displaced from the primary vertex. This paper presents, for the first time, an implementation and preliminary performance results using NVIDIA Tesla C2075 and K20c GPUs.At the Large Hadron Collider (LHC), the trigger systems for the detectors must be able to process a very large amount of data in a very limited amount of time, so that the nominal collision rate of 40 MHz can be reduced to a data rate that can be stored and processed in a reasonable amount of time. This need for high performance places very stringent requirements on the complexity of the algorithms that can be used for identifying events of interest in the trigger system, which potentially limits the ability to trigger on signatures of various new physics models. In this paper, we present an alternative tracking algorithm, based on the Hough transform, which avoids many of the problems associated with the standard combinatorial track finding currently used. The Hough transform is also well-adapted for Graphics Processing Unit (GPU)-based computing, and such GPU-based systems could be easily integrated into the existing High-Level Trigger (HLT). This algorithm offers the ability to trigger on topological signatures of new physics currently not practical to reconstruct, such as events with jets or black holes significantly displaced from the primary vertex. This paper presents, for the first time, an implementation and preliminary performance results using NVIDIA Tesla C2075 and K20c GPUs.At the Large Hadron Collider (LHC), the trigger systems for the detectors must be able to process a very large amount of data in a very limited amount of time, so that the nominal collision rate of 40 MHz can be reduced to a data rate that can be stored and processed in a reasonable amount of time. This need for high performance places very stringent requirements on the complexity of the algorithms that can be used for identifying events of interest in the trigger system, which potentially limits the ability to trigger on signatures of various new physics models. In this paper, we present an alternative tracking algorithm, based on the Hough transform, which avoids many of the problems associated with the standard combinatorial track finding currently used. The Hough transform is also well-adapted for Graphics Processing Unit (GPU)-based computing, and such GPU-based systems could be easily integrated into the existing High-Level Trigger (HLT). This algorithm offers the ability to trigger on topological signatures of new physics currently not practical to reconstruct, such as events with jets or black holes significantly displaced from the primary vertex. This paper presents, for the first time, an implementation and preliminary performance results using NVIDIA Tesla C2075 and K20c GPUs.arXiv:1311.2769oai:cds.cern.ch:16277152013-11-12
spellingShingle Detectors and Experimental Techniques
Lujan, P.
Halyo, V.
Hunt, A.
Jindal, P.
LeGresley, P.
GPU Enhancement of the Trigger to Extend Physics Reach at the LHC
title GPU Enhancement of the Trigger to Extend Physics Reach at the LHC
title_full GPU Enhancement of the Trigger to Extend Physics Reach at the LHC
title_fullStr GPU Enhancement of the Trigger to Extend Physics Reach at the LHC
title_full_unstemmed GPU Enhancement of the Trigger to Extend Physics Reach at the LHC
title_short GPU Enhancement of the Trigger to Extend Physics Reach at the LHC
title_sort gpu enhancement of the trigger to extend physics reach at the lhc
topic Detectors and Experimental Techniques
url https://dx.doi.org/10.1088/1742-6596/513/1/012019
http://cds.cern.ch/record/1627715
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