Future of computing at the Large Hadron Collider

TL;DR

This paper reviews computing challenges at the LHC, explores various architectures for data processing, and introduces new algorithms optimized for multi-core processors like GPUs and IPUs to address future data and power demands.

Contribution

It provides an overview of computing techniques in LHCb and introduces three new event reconstruction algorithms optimized for modern multi-core architectures.

Findings

GPUs and IPUs outperform CPUs for HEP tasks

New algorithms show improved processing efficiency

Multi-core architectures are essential for future HEP computing

Abstract

High energy physics (HEP) experiments at the LHC generate data at a rate of $\mathcal{O}(10)$ Terabits per second. This data rate is expected to exponentially increase as experiments will be upgraded in the future to achieve higher collision energies. The increasing size of particle physics datasets combined with the plateauing single-core CPU performance is expected to create a four-fold shortage in computing power by 2030. This makes it necessary to investigate alternate computing architectures to cope with the next generation of HEP experiments. This study provides an overview of different computing techniques used in the LHCb experiment (trigger, track reconstruction, vertex reconstruction, particle identification). Furthermore, this research led to the creation of three event reconstruction algorithms for the LHCb experiment. These algorithms are benchmarked on various computing architectures such as the CPU, GPU, and a new type of processor called the IPU, each roughly containing $\mathcal{O}(10)$, $\mathcal{O}(1000)$, and $\mathcal{O}(1000)$ cores respectively. This research indicates that multi-core architectures such as GPUs and IPUs are better suited for computationally intensive tasks within HEP experiments.