<abstract xmlns="http://www.w3.org/1999/xhtml"> The numerical simulation allows to study the high energy particle physics. It plays important of role in the reconstruction and analyze of these experimental and theoretical researches. This requires a computer background with a large capacity. Jet physics is an intensively researched area, where the factorization process can be solved by algorithmic solutions. We studied parallelization of the kt cluster algorithms. This method allows to know the development of particles due to the collision of highenergy nucleus-nucleus. The Alice offline library contains the required modules to simulate the ALICE detector that is a dedicated Pb-Pb detector. Using this simulation we can generate input particles, that we can further analyzed by clustering them, reconstructing their jet structure. The FastJet toolkit is an efficient C++ implementation of the most widely used jet clustering algorithms, among them the kt clustering. Parallelizing the standard non-optimized version of this algorithm utilizing the available CPU architecture a 1:6 times faster runtime was achieved, paving the way to drastic performance increase using many-core architectures. </abstract>

The numerical simulation allows to study the high energy particle physics. It plays important of role in the reconstruction and analyze of these experimental and theoretical researches. This requires a computer background with a large capacity. Jet physics is an intensively researched area, where the factorization process can be solved by algorithmic solutions. We studied parallelization of the kt cluster algorithms. This method allows to know the development of particles due to the collision of highenergy nucleus-nucleus. The Alice offline library contains the required modules to simulate the ALICE detector that is a dedicated Pb-Pb detector. Using this simulation we can generate input particles, that we can further analyzed by clustering them, reconstructing their jet structure. The FastJet toolkit is an efficient C++ implementation of the most widely used jet clustering algorithms, among them the kt clustering. Parallelizing the standard non-optimized version of this algorithm utilizing the available CPU architecture a 1:6 times faster runtime was achieved, paving the way to drastic performance increase using many-core architectures.

Parallel kt jet clustering algorithm

Acta Universitatis Sapientiae, Informatica

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Dedicated to the memory of Antal Iványi

The numerical simulation allows to study the high energy particle physics. It plays important of role in the reconstruction and analyze of these experimental...

Parallel k_t jet clustering algorithm

Online veröffentlicht: 01. Aug. 2017

Seitenbereich: 49 - 64

Eingereicht: 03. Apr. 2017

DOI: https://doi.org/10.1515/ausi-2017-0004

Schlüsselwörter
jet, cluster algorithm, database of experimenthal particle physics parallel computing, multi-core, C++11

© 2017

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Parallel kt jet clustering algorithm

Online veröffentlicht: 01. Aug. 2017

Seitenbereich: 49 - 64

Eingereicht: 03. Apr. 2017

DOI: https://doi.org/10.1515/ausi-2017-0004

Schlüsselwörterjet, cluster algorithm, database of experimenthal particle physics parallel computing, multi-core, C++11

© 2017

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Parallel k_t jet clustering algorithm

Schlüsselwörter
jet, cluster algorithm, database of experimenthal particle physics parallel computing, multi-core, C++11