Generators and the (Accelerated) Future

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.

11 pages, 8 figures, 5 tables, plenary proceedings for the 20th International Workshop on Advanced Computing and Analysis Techniques in Physics Research

References in corpus (8)

• The automated computation of tree-level and next-to-leading order differential cross sections, and their matching to parton shower simulations
• GoSam-2.0: a tool for automated one-loop calculations within the Standard Model and beyond
• Automating dipole subtraction for QCD NLO calculations
• A factorisation-aware Matrix element emulator
• Fast simulation of muons produced at the SHiP experiment using Generative Adversarial Networks
• Phase Space Sampling and Inference from Weighted Events with Autoregressive Flows
• Design and engineering of a simplified workflow execution for the MG5aMC event generator on GPUs and vector CPUs
• MadFlow: automating Monte Carlo simulation on GPU for particle physics processes