Highly-parallelized simulation of a pixelated LArTPC on a GPU
File(s)Abed_Abud_2023_J._Inst._18_P04034.pdf (15.26 MB)
Published verison
Author(s)
Type
Journal Article
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 projection 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 103 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.
(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 projection 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 103 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.
Date Issued
2023-04
Date Acceptance
2023-03-28
Citation
Journal of Instrumentation, 2023, 18 (4)
ISSN
1748-0221
Publisher
IOP Publishing
Journal / Book Title
Journal of Instrumentation
Volume
18
Issue
4
Copyright Statement
© 2023 CERN. Published by IOP Publishing Ltd on behalf of Sissa
Medialab. Original content from this work may be used under the terms
of the Creative Commons Attribution 4.0 licence. Any further distribution of this work
must maintain attribution to the author(s) and the title of the work, journal citation
and DOI.
Medialab. Original content from this work may be used under the terms
of the Creative Commons Attribution 4.0 licence. Any further distribution of this work
must maintain attribution to the author(s) and the title of the work, journal citation
and DOI.
License URL
Identifier
https://iopscience.iop.org/article/10.1088/1748-0221/18/04/P04034
Subjects
Detector modelling and simulations II (electric fields, charge transport, multiplication, and induction, pulse formation, electron emission, etc)
ELECTRONS
Instruments & Instrumentation
LIQUID ARGON
LUMINESCENCE
Nobleliquid detectors (scintillation, ionization, double-phase)
Science & Technology
Simulation methods and programs
Technology
Time projection Chambers (TPC)
Publication Status
Published
Article Number
P04034
Date Publish Online
2023-04-26