Continuous normalizing flows improve unweighting efficiency in Monte Carlo event generation for high-jet-multiplicity collider processes by factors up to 184, with wall-time gains of about ten when combined with coupling-layer flows.
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GAPS v2 is a GPU-accelerated parton shower for initial and final state emissions with NLO matching that achieves speed and energy performance on par with a 96-core CPU cluster for NLO Z production at the LHC.
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Monte Carlo Event Generation with Continuous Normalizing Flows
Continuous normalizing flows improve unweighting efficiency in Monte Carlo event generation for high-jet-multiplicity collider processes by factors up to 184, with wall-time gains of about ten when combined with coupling-layer flows.
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An NLO-Matched Initial and Final State Parton Shower on a GPU
GAPS v2 is a GPU-accelerated parton shower for initial and final state emissions with NLO matching that achieves speed and energy performance on par with a 96-core CPU cluster for NLO Z production at the LHC.