GPGPU for track finding in High Energy Physics

TL;DR

This paper explores using GPGPU technology to accelerate track pattern recognition in high energy physics, aiming to handle increased data rates and complex environments at the LHC with faster processing.

Contribution

It introduces a parallel implementation of a Hough Transform-based track recognition algorithm optimized for GPGPU, enhancing speed for high-luminosity LHC conditions.

Findings

Significant reduction in execution time with GPU implementation

Effective parallelization of the Hough Transform algorithm

Potential for real-time track finding in high-energy physics experiments

Abstract

The LHC experiments are designed to detect large amount of physics events produced with a very high rate. Considering the future upgrades, the data acquisition rate will become even higher and new computing paradigms must be adopted for fast data-processing: General Purpose Graphics Processing Units (GPGPU) is a novel approach based on massive parallel computing. The intense computation power provided by Graphics Processing Units (GPU) is expected to reduce the computation time and to speed-up the low-latency applications used for fast decision taking. In particular, this approach could be hence used for high-level triggering in very complex environments, like the typical inner tracking systems of the multi-purpose experiments at LHC, where a large number of charged particle tracks will be produced with the luminosity upgrade. In this article we discuss a track pattern recognition algorithm based on the Hough Transform, where a parallel approach is expected to reduce dramatically the execution time.