Towards the automation of Monte Carlo simulation on GPU for particle physics processes

Abstract

In this proceedings we present MadFlow, a new framework for the automation of Monte Carlo (MC) simulation on graphics processing units (GPU) for particle physics processes. In order to automate MC simulation for a generic number of processes, we design a program which provides to the user the possibility to simulate custom processes through the MadGraph5_aMC@NLO framework. The pipeline includes a first stage where the analytic expressions for matrix elements and phase space are generated and exported in a GPU-like format. The simulation is then performed using the VegasFlow and PDFFlow libraries which deploy automatically the full simulation on systems with different hardware acceleration capabilities, such as multi-threading CPU, single-GPU and multi-GPU setups. We show some preliminary results for leading-order simulations on different hardware configurations.

arXiv comment6 pages, 3 figures, to appear in the vCHEP 2021 conference proceedings

References in corpus (9)

• The automated computation of tree-level and next-to-leading order differential cross sections, and their matching to parton shower simulations
• MadGraph 5 : Going Beyond
• Event generation with SHERPA 1.1
• UFO - The Universal FeynRules Output
• The automation of next-to-leading order electroweak calculations
• ALOHA: Automatic Libraries Of Helicity Amplitudes for Feynman diagram computations
• VegasFlow: accelerating Monte Carlo simulation across multiple hardware platforms
• NNLO QCD Corrections to Jet Production in Charged Current Deep Inelastic Scattering
• VegasFlow: accelerating Monte Carlo simulation across platforms

Cited by in corpus (5)

• Event Generators for High-Energy Physics Experiments
• Design and engineering of a simplified workflow execution for the MG5aMC event generator on GPUs and vector CPUs
• A Portable Parton-Level Event Generator for the High-Luminosity LHC
• MadFlow: automating Monte Carlo simulation on GPU for particle physics processes
• Constraining the Higgs Potential with Neural Simulation-based Inference for Di-Higgs Production