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The compute efficiency of Monte-Carlo event generators for the Large Hadron Collider is expected to become a major bottleneck for simulations in the high-luminosity phase. Aiming at the development of a full-fledged generator for modern GPUs, we study the performance of various recursive strategies to compute multi-gluon tree-level amplitudes. We investigate the scaling of the algorithms on both CPU and GPU hardware. Finally, we provide practical recommendations as well as baseline implementations for the development of future simulation programs. The GPU implementations can be found at: https://www.gitlab.com/ebothmann/blockgen-archive.
Cited by 4
Janßen et al., Unweighting multijet event generation using factorisation-aware neural networks
SciPost Phys. 15, 107 (2023) [Crossref]
Bothmann et al., Efficient phase-space generation for hadron collider event simulation
SciPost Phys. 15, 169 (2023) [Crossref]
Yallup et al., Exploring phase space with nested sampling
Eur. Phys. J. C 82, 678 (2022) [Crossref]
Bothmann et al., Codebase release 1.0 for BlockGen
SciPost Phys. Codebases, 3-r1.0 (2022) [Crossref]
Authors / Affiliations: mappings to Contributors and OrganizationsSee all Organizations.
- 1 Georg-August-Universität Göttingen / University of Göttingen [GAU]
- 2 Fermi National Accelerator Laboratory [Fermilab]