Highly-parallelized simulation of a pixelated LArTPC on a GPU

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 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.

Authors: Abud, AA; Abi, B; Acciarri, R; Acero, MA; Adames, MR

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: IOP Publishing
• Journal: Journal of Instrumentation
• Volume: 18
• Issue: 04
• Pages: P04034-P04034
• Publication date: 2023-04-26
• DOI: 10.1088/1748-0221/18/04/p04034
• EISSN: 1748-0221
• ISSN: 1748-0221
• Language: English
• Keywords: Operating system; Python (programming language); General-purpose computing on graphics processing units; Detector; Rendering (computer graphics); Computer graphics (images); Artificial intelligence; Parallel computing; CUDA; Graphics; Computational science; Monte Carlo method; Computer science; Speedup; Pixel