| Issue |
EPJ Web Conf.
Volume 251, 2021
25th International Conference on Computing in High Energy and Nuclear Physics (CHEP 2021)
|
|
|---|---|---|
| Article Number | 03022 | |
| Number of page(s) | 6 | |
| Section | Offline Computing | |
| DOI | https://doi.org/10.1051/epjconf/202125103022 | |
| Published online | 23 August 2021 | |
https://doi.org/10.1051/epjconf/202125103022
MadFlow: towards the automation of Monte Carlo simulation on GPU for particle physics processes
1 Dipartimento di Fisica, Università degli Studi di Milano and INFN Sezione di Milano, Milan, Italy
2 CERN, Theoretical Physics Department and OpenLab, CH-1211 Geneva 23, Switzerland
3 Quantum Research Centre, Technology Innovation Institute, Abu Dhabi, UAE
* e-mail: stefano.carrazza@unimi.it
** e-mail: juan.cruz@mi.infn.it
*** e-mail: marco.rossi@cern.ch
**** e-mail: marco.zaro@mi.infn.it
Published online: 23 August 2021
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 Mad-Graph5_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.
© The Authors, published by EDP Sciences, 2021
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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