Data-parallel leading-order event generation in MadGraph5_aMC@NLO
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866911085293993984 |
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| author | Hageböck, Stephan Massaro, Daniele Mattelaer, Olivier Roiser, Stefan Valassi, Andrea Wettersten, Zenny |
| author_facet | Hageböck, Stephan Massaro, Daniele Mattelaer, Olivier Roiser, Stefan Valassi, Andrea Wettersten, Zenny |
| contents | The CUDACPP plugin for MadGraph5_aMC@NLO aims to accelerate leading order tree-level event generation by providing the MadEvent event generator with data-parallel helicity amplitudes. These amplitudes are written in templated C++ and CUDA, allowing them to be compiled for CPUs supporting SSE4, AVX2, and AVX-512 instruction sets as well as CUDA- and HIP-enabled GPUs. Using SIMD instruction sets, CUDACPP-generated amplitude routines routines are shown to speed up linearly with SIMD register size, and GPU offloading is shown to provide acceleration beyond that of SIMD instructions. Additionally, the resulting speed-up in event generation perfectly aligns with predictions from measured runtime fractions spent in amplitude routines, and proper GPU utilisation can speed up high-multiplicity QCD processes by an order of magnitude when compared to optimal CPU usage in server-grade CPUs. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_21039 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Data-parallel leading-order event generation in MadGraph5_aMC@NLO Hageböck, Stephan Massaro, Daniele Mattelaer, Olivier Roiser, Stefan Valassi, Andrea Wettersten, Zenny High Energy Physics - Phenomenology High Energy Physics - Experiment Computational Physics The CUDACPP plugin for MadGraph5_aMC@NLO aims to accelerate leading order tree-level event generation by providing the MadEvent event generator with data-parallel helicity amplitudes. These amplitudes are written in templated C++ and CUDA, allowing them to be compiled for CPUs supporting SSE4, AVX2, and AVX-512 instruction sets as well as CUDA- and HIP-enabled GPUs. Using SIMD instruction sets, CUDACPP-generated amplitude routines routines are shown to speed up linearly with SIMD register size, and GPU offloading is shown to provide acceleration beyond that of SIMD instructions. Additionally, the resulting speed-up in event generation perfectly aligns with predictions from measured runtime fractions spent in amplitude routines, and proper GPU utilisation can speed up high-multiplicity QCD processes by an order of magnitude when compared to optimal CPU usage in server-grade CPUs. |
| title | Data-parallel leading-order event generation in MadGraph5_aMC@NLO |
| topic | High Energy Physics - Phenomenology High Energy Physics - Experiment Computational Physics |
| url | https://arxiv.org/abs/2507.21039 |