# Computational challenges for MC event generation

## TL;DR

This paper reviews the rapid growth in computational complexity of Monte Carlo event generation for particle physics, highlighting bottlenecks and proposing new computational strategies to mitigate rising costs at the LHC.

## Contribution

It provides a comprehensive analysis of current computational challenges in MC event generation and explores innovative solutions like new architectures and machine learning.

## Key findings

- MC computational costs are rising faster than speed improvements
- Current bottlenecks limit the physics potential of HL-LHC analyses
- Emerging computing methods may help reduce costs

## Abstract

The sophistication of fully exclusive MC event generation has grown at an extraordinary rate since the start of the LHC era, but has been mirrored by a similarly extraordinary rise in the CPU cost of state-of-the-art MC calculations. The reliance of experimental analyses on these calculations raises the disturbing spectre of MC computations being a leading limitation on the physics impact of the HL-LHC, with MC trends showing more signs of further cost-increases rather than the desired speed-ups. I review the methods and bottlenecks in MC computation, and areas where new computing architectures, machine-learning methods, and social structures may help to avert calamity.