Generators and the (Accelerated) Future

TL;DR

This paper discusses the challenges and ongoing efforts to improve the computational efficiency of event generators for the High Luminosity LHC, focusing on GPU implementation and machine learning techniques.

Contribution

It reviews current approaches such as GPU acceleration and machine learning methods to enhance event generator efficiency for future high-precision experiments.

Findings

GPU implementation of event generators shows promising speedups

Machine learning techniques can optimize matrix element calculations

Efforts reduce negative weight events in simulations

Abstract

With the High Luminosity LHC coming online in the near future, event generators will need to provide very large event samples to match the experimental precision. Currently, the estimated cost to generate these events exceeds the computing budget of the LHC experiments. To address these issues, the computing efficiency of event generators need to be improved. Many different approaches are being taken to achieve this goal. I will cover the ongoing work on implementing event generators on the GPUs, machine learning the matrix element, machine learning the phase space, and minimizing the number of negative weight events.