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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).
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S. Carrazza, J. Cruz-Martinez, M. Rossi, M. Zaro, Towards the automation of Monte Carlo simulation on GPU for particle physics processes , in 25th International Conference on Computing in High-Energy and Nuclear Physics (2021), 2105.10529
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J. Alwall, R. Frederix, S. Frixione, V. Hirschi, F. Maltoni, O. Mattelaer, H.S. Shao, T. Stelzer, P. Torrielli, M. Zaro, JHEP 07
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M. Abadi, A. Agarwal, P. Barham, E. Brevdo, Z. Chen, C. Citro, G.S. Corrado, A. Davis, J. Dean, M. Devin et al., TensorFlow: Large-scale machine learning on heterogeneous systems (2015), software available from tensorflow.org, http://tensorflow.org/
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Madgraph 4 gpu , https://github.com/madgraph5/madgraph4gpu
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2021
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O. Mattelaer, K. Ostrolenk, Eur. Phys. J. C 81
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C. Degrande, C. Duhr, B. Fuks, D. Grellscheid, O. Mattelaer, T. Reiter, Comput. Phys. Commun. 183
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P. de Aquino, W. Link, F. Maltoni, O. Mattelaer, T. Stelzer, Comput. Phys. Commun. 183
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