Speeding up MadGraph5_aMC@NLO

Mattelaer, Olivier; Ostrolenk, Kiran

doi:10.1140/epjc/s10052-021-09204-7

Cited by 15 publications

(16 citation statements)

References 53 publications

(101 reference statements)

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“…2. In this work, we simulate events using MadGraph5 aMC@NLO v3.1.0 [77][78][79][80], with UFO file implemented in FeynRules 2.3.32 [81,82]. For the mass range [2 MeV,200 MeV] that we are interested in, the cross section ( e) −2 σ varies from 10 9 pb to 10 5 pb, decreasing as mass increases.…”

Section: A Dark Photon Production In µ E Collisionmentioning

confidence: 99%

Dark Photons and Displaced Vertices at the MUonE Experiment

Galon¹,

Shih²,

R.³

2022

Preprint

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MUonE is a proposed experiment designed to measure the hadronic vacuum polarization contribution to muon g − 2 through elastic µ − e scattering. As such it employs an extremely high-resolution tracking apparatus. We point out that this makes MUonE also a very promising experiment to search for displaced vertices from light, weakly-interacting new particles. We demonstrate its potential by showing how it has excellent sensitivity to dark photons in the mass range 10 MeV ≤ m A ≤ 100 MeV and kinetic mixing parameter 10 −5 ≤ e ≤ 10 −3 , through the process µ − e − → µ − e − A followed by A → e + e − .

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Section: A Dark Photon Production In µ E Collisionmentioning

confidence: 99%

Dark Photons and Displaced Vertices at the MUonE Experiment

Galon¹,

Shih²,

R.³

2022

Preprint

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“…Therefore, for processes with many particles in the final state or when using the tree level amplitudes for Next-to-Leading Order (NLO) calculations writing an specialized phase-space is recommended [27]. For some recent developments on this matter, see also [28].…”

Section: Scopementioning

confidence: 99%

MadFlow: automating Monte Carlo simulation on GPU for particle physics processes

Carrazza,

Cruz-Martinez,

Rossi

et al. 2021

Preprint

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We present MadFlow, a first general multi-purpose framework for Monte Carlo (MC) event simulation of particle physics processes designed to take full advantage of hardware accelerators, in particular, graphics processing units (GPUs). The automation process of generating all the required components for MC simulation of a generic physics process and its deployment on hardware accelerator is still a big challenge nowadays. In order to solve this challenge, we design a workflow and code library which provides to the user the possibility to simulate custom processes through the MadGraph5 aMC@NLO framework and a plugin for the generation and exporting of specialized code in a GPU-like format. The exported code includes analytic expressions for matrix elements and phase space. The simulation is 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. The package also provides an asynchronous unweighted events procedure to store simulation results. Crucially, although only Leading Order is automatized, the library provides all ingredients necessary to build full complex Monte Carlo simulators in a modern, extensible and maintainable way. We show simulation results at leading-order for multiple processes on different hardware configurations.

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“…The general techniques used in MG5aMC to integrate a LO partonic cross section, and to obtain a set of unweighted events from it, have been inherited from MadGraph5 [15]. This strategy has then undergone successive optimisations, including some very recent ones [16]. The core of the process is the calculation of tree-level invariant amplitudes and "matrix elements" (ME) from the set of all relevant Feynman diagrams.…”

Section: Brief Description Of the Mg5amc Softwarementioning

confidence: 99%

“…The phase space points where the ME calculation functions are evaluated are determined by a Monte Carlo (MC) integrator, using a sampling algorithm based on random number generation. The integrator is the component that drives the whole process of event generation, cross section integration and event unweighting: in the production version of MG5aMC, this is the Fortran package MadEvent, which uses an optimised phase space sampling algorithm based on single diagram enhancement [16,21].…”

Section: Brief Description Of the Mg5amc Softwarementioning

confidence: 99%

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Design and engineering of a simplified workflow execution for the MG5aMC event generator on GPUs and vector CPUs

Valassi,

Roiser,

Mattelaer

et al. 2021

Preprint

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Physics event generators are essential components of the data analysis software chain of high energy physics experiments, and important consumers of their CPU resources. Improving the software performance of these packages on modern hardware architectures, such as those deployed at HPC centers, is essential in view of the upcoming HL-LHC physics programme. In this paper, we describe an ongoing activity to reengineer the Madgraph5_aMC@NLO physics event generator, primarily to port it and allow its efficient execution on GPUs, but also to modernize it and optimize its performance on vector CPUs. We describe the motivation, engineering process and software architecture design of our developments, as well as the current challenges and future directions for this project. This paper is based on our submission to vCHEP2021 in March 2021, complemented with a few preliminary results that we presented during the conference. Further details and updated results will be given in later publications.

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Speeding up MadGraph5_aMC@NLO

Cited by 15 publications

References 53 publications

Dark Photons and Displaced Vertices at the MUonE Experiment

Dark Photons and Displaced Vertices at the MUonE Experiment

MadFlow: automating Monte Carlo simulation on GPU for particle physics processes

Design and engineering of a simplified workflow execution for the MG5aMC event generator on GPUs and vector CPUs

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