Computing trends using graphic processor in high energy physics

TL;DR

This paper discusses how GPUs are increasingly used in high energy physics to analyze large datasets more efficiently, highlighting current trends and advantages over traditional CPU methods.

Contribution

It provides an overview of the current status and emerging trends in utilizing GPU computing for high energy physics data analysis.

Findings

GPU computing offers significant speedups in data analysis.

GPU-based tools are increasingly adopted in high energy physics.

Cost-effective high-performance computing is achievable with GPUs.

Abstract

One of the main challenges in Heavy Energy Physics is to make fast analysis of high amount of experimental and simulated data. At LHC-CERN one p-p event is approximate 1 Mb in size. The time taken to analyze the data and obtain fast results depends on high computational power. The main advantage of using GPU(Graphic Processor Unit) programming over traditional CPU one is that graphical cards bring a lot of computing power at a very low price. Today a huge number of application(scientific, financial etc) began to be ported or developed for GPU, including Monte Carlo tools or data analysis tools for High Energy Physics. In this paper, we'll present current status and trends in HEP using GPU.