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UID:pretalx-juliacon-2026-DAY8TS@pretalx.com
DTSTART;TZID=CET:20260812T110000
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DESCRIPTION:In this talk\, we present techniques for implementing efficient
  GPU-based random sampling\nalgorithms\, including rejection sampling and 
 multi-stage sampling pipelines using\nKernelAbstractions.jl. We discuss st
 rategies to avoid costly synchronizations between host\nand device\, manag
 e divergent execution flows\, and schedule heterogeneous\nworkloads entire
 ly on device. Particular attention is given to structuring rejection\nsamp
 ling and sequential transport algorithms in ways that preserve GPU occupan
 cy while\nmaintaining statistical correctness.\n\nTo demonstrate the appli
 cability of these techniques\, we present results from two Julia\npackages
  developed for plasma and high-energy physics applications. The first\,\nQ
 EDevents.jl\, focuses on Monte-Carlo event generation for high-multiplicit
 y\nscattering processes in quantum electrodynamics\, building on the gener
 ic sampling\nframework RejectionSamplers.jl and the QuantumElectrodynamics
 .jl ecosystem. The second\,\nPhotonTransport.jl implements a Monte-Carlo p
 hoton transport code for warm-dense matter.\nTogether\, these case studies
  illustrate how Julia enables both the expressive\nimplementation of paral
 lel sampling algorithms and near-peak accelerator performance\,\nwhile mai
 ntaining portability across hardware backends.
DTSTAMP:20260428T150904Z
LOCATION:Room 5
SUMMARY:Performance-Portable Random Sampling in Julia: Event Generation and
  Particle Transport on GPUs - Uwe Hernandez Acosta
URL:https://pretalx.com/juliacon-2026/talk/DAY8TS/
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