Optimizing the Hit Finding Algorithm for Liquid Argon TPC Neutrino Detectors Using Parallel Architectures

TL;DR

This paper presents a parallelized implementation of the hit finding algorithm for Liquid Argon TPC neutrino detectors, significantly improving processing speed to handle larger data volumes in upcoming experiments.

Contribution

It introduces a parallelization approach for the LArTPC hit finding algorithm, achieving substantial speedups and integration into existing frameworks for future large-scale neutrino experiments.

Findings

Vectorization yields 2x speedup.

Multi-threading achieves 30-100x speedup.

Integrated version is 10x faster on serial execution.

Abstract

Neutrinos are particles that interact rarely, so identifying them requires large detectors which produce lots of data. Processing this data with the computing power available is becoming even more difficult as the detectors increase in size to reach their physics goals. Liquid argon time projection chamber (LArTPC) neutrino experiments are expected to grow in the next decade to have 100 times more wires than in currently operating experiments, and modernization of LArTPC reconstruction code, including parallelization both at data- and instruction-level, will help to mitigate this challenge. The LArTPC hit finding algorithm is used across multiple experiments through a common software framework. In this paper we discuss a parallel implementation of this algorithm. Using a standalone setup we find speed up factors of two times from vectorization and 30--100 times from multi-threading on Intel architectures. The new version has been incorporated back into the framework so that it can be used by experiments. On a serial execution, the integrated version is about 10 times faster than the previous one and, once parallelization is enabled, further speedups comparable to the standalone program are achieved.