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Highly-parallelized simulation of a pixelated LArTPC on a GPU

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Highly-parallelized simulation of a pixelated LArTPC on a GPU. / DUNE Collaboration ; Blake, A.; Brailsford, D. et al.
In: Journal of Instrumentation, Vol. 18, P04034, 26.04.2023.

Research output: Contribution to Journal/MagazineJournal articlepeer-review

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DUNE Collaboration, Blake A, Brailsford D, Cross R, Mouster G, Nowak JA et al. Highly-parallelized simulation of a pixelated LArTPC on a GPU. Journal of Instrumentation. 2023 Apr 26;18:P04034. doi: 10.1088/1748-0221/18/04/P04034

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DUNE Collaboration ; Blake, A. ; Brailsford, D. et al. / Highly-parallelized simulation of a pixelated LArTPC on a GPU. In: Journal of Instrumentation. 2023 ; Vol. 18.

Bibtex

@article{199f0830648d456990c7926ef16da44f,
title = "Highly-parallelized simulation of a pixelated LArTPC on a GPU",
abstract = "The rapid development of general-purpose computing on graphics processing units (GPGPU) is allowing the implementation of highly-parallelized Monte Carlo simulation chains for particle physics experiments. This technique is particularly suitable for the simulation of a pixelated charge readout for time projection chambers, given the large number of channels that this technology employs. Here we present the first implementation of a full microphysical simulator of a liquid argon time project chamber (LArTPC) equipped with light readout and pixelated charge readout, developed for the DUNE Near Detector. The software is implemented with an end-to-end set of GPU-optimized algorithms. The algorithms have been written in Python and translated into CUDA kernels using Numba, a just-in-time compiler for a subset of Python and NumPy instructions. The GPU implementation achieves a speed up of four orders of magnitude compared with the equivalent CPU version. The simulation of the current induced on $10^3$ pixels takes around 1 ms on the GPU, compared with approximately 10 s on the CPU. The results of the simulation are compared against data from a pixel-readout LArTPC prototype. ",
keywords = "physics.comp-ph, physics.ins-det",
author = "{DUNE Collaboration} and Abud, {A. Abed} and B. Abi and R. Acciarri and Acero, {M. A.} and Adames, {M. R.} and G. Adamov and M. Adamowski and D. Adams and M. Adinolfi and C. Adriano and A. Aduszkiewicz and J. Aguilar and Z. Ahmad and J. Ahmed and B. Aimard and F. Akbar and K. Allison and Monsalve, {S. Alonso} and M. Alrashed and C. Alt and A. Alton and R. Alvarez and P. Amedo and J. Anderson and Andrade, {D. A.} and C. Andreopoulos and M. Andreotti and Andrews, {M. P.} and F. Andrianala and S. Andringa and N. Anfimov and Campanelli, {W. L. Anic{\'e}zio} and A. Ankowski and M. Antoniassi and M. Antonova and A. Antoshkin and S. Antusch and A. Aranda-Fernandez and L. Arellano and Arnold, {L. O.} and Arroyave, {M. A.} and J. Asaadi and A. Ashkenazi and A. Blake and D. Brailsford and R. Cross and G. Mouster and Nowak, {J. A.} and P. Ratoff",
year = "2023",
month = apr,
day = "26",
doi = "10.1088/1748-0221/18/04/P04034",
language = "English",
volume = "18",
journal = "Journal of Instrumentation",
issn = "1748-0221",
publisher = "Institute of Physics Publishing",

}

RIS

TY - JOUR

T1 - Highly-parallelized simulation of a pixelated LArTPC on a GPU

AU - DUNE Collaboration

AU - Abud, A. Abed

AU - Abi, B.

AU - Acciarri, R.

AU - Acero, M. A.

AU - Adames, M. R.

AU - Adamov, G.

AU - Adamowski, M.

AU - Adams, D.

AU - Adinolfi, M.

AU - Adriano, C.

AU - Aduszkiewicz, A.

AU - Aguilar, J.

AU - Ahmad, Z.

AU - Ahmed, J.

AU - Aimard, B.

AU - Akbar, F.

AU - Allison, K.

AU - Monsalve, S. Alonso

AU - Alrashed, M.

AU - Alt, C.

AU - Alton, A.

AU - Alvarez, R.

AU - Amedo, P.

AU - Anderson, J.

AU - Andrade, D. A.

AU - Andreopoulos, C.

AU - Andreotti, M.

AU - Andrews, M. P.

AU - Andrianala, F.

AU - Andringa, S.

AU - Anfimov, N.

AU - Campanelli, W. L. Anicézio

AU - Ankowski, A.

AU - Antoniassi, M.

AU - Antonova, M.

AU - Antoshkin, A.

AU - Antusch, S.

AU - Aranda-Fernandez, A.

AU - Arellano, L.

AU - Arnold, L. O.

AU - Arroyave, M. A.

AU - Asaadi, J.

AU - Ashkenazi, A.

AU - Blake, A.

AU - Brailsford, D.

AU - Cross, R.

AU - Mouster, G.

AU - Nowak, J. A.

AU - Ratoff, P.

PY - 2023/4/26

Y1 - 2023/4/26

N2 - The rapid development of general-purpose computing on graphics processing units (GPGPU) is allowing the implementation of highly-parallelized Monte Carlo simulation chains for particle physics experiments. This technique is particularly suitable for the simulation of a pixelated charge readout for time projection chambers, given the large number of channels that this technology employs. Here we present the first implementation of a full microphysical simulator of a liquid argon time project chamber (LArTPC) equipped with light readout and pixelated charge readout, developed for the DUNE Near Detector. The software is implemented with an end-to-end set of GPU-optimized algorithms. The algorithms have been written in Python and translated into CUDA kernels using Numba, a just-in-time compiler for a subset of Python and NumPy instructions. The GPU implementation achieves a speed up of four orders of magnitude compared with the equivalent CPU version. The simulation of the current induced on $10^3$ pixels takes around 1 ms on the GPU, compared with approximately 10 s on the CPU. The results of the simulation are compared against data from a pixel-readout LArTPC prototype.

AB - The rapid development of general-purpose computing on graphics processing units (GPGPU) is allowing the implementation of highly-parallelized Monte Carlo simulation chains for particle physics experiments. This technique is particularly suitable for the simulation of a pixelated charge readout for time projection chambers, given the large number of channels that this technology employs. Here we present the first implementation of a full microphysical simulator of a liquid argon time project chamber (LArTPC) equipped with light readout and pixelated charge readout, developed for the DUNE Near Detector. The software is implemented with an end-to-end set of GPU-optimized algorithms. The algorithms have been written in Python and translated into CUDA kernels using Numba, a just-in-time compiler for a subset of Python and NumPy instructions. The GPU implementation achieves a speed up of four orders of magnitude compared with the equivalent CPU version. The simulation of the current induced on $10^3$ pixels takes around 1 ms on the GPU, compared with approximately 10 s on the CPU. The results of the simulation are compared against data from a pixel-readout LArTPC prototype.

KW - physics.comp-ph

KW - physics.ins-det

U2 - 10.1088/1748-0221/18/04/P04034

DO - 10.1088/1748-0221/18/04/P04034

M3 - Journal article

VL - 18

JO - Journal of Instrumentation

JF - Journal of Instrumentation

SN - 1748-0221

M1 - P04034

ER -