Uwe Hernandez Acosta
I am a particle physicist by training, currently working on the theoretical side of strong laser interactions and matter under extreme conditions at the Helmholtz-Zentrum Dresden-Rossendorf. For the past five years, I have conducted all my research in Julia, and I am the primary author of several Julia packages, including the QuantumElectrodynamics.jl framework, JuliaXRTS, and a maintainer at the JuliaHEP GitHub organization. In addition, for the past three years, I have served as one of the conveners of the JuliaHEP working group within the HEP Software Foundation, making me an active member of the Julia community in high-energy physics.
Session
In this talk, we present techniques for implementing efficient GPU-based random sampling
algorithms, including rejection sampling and multi-stage sampling pipelines using
KernelAbstractions.jl. We discuss strategies to avoid costly synchronizations between host
and device, manage divergent execution flows, and schedule heterogeneous
workloads entirely on device. Particular attention is given to structuring rejection
sampling and sequential transport algorithms in ways that preserve GPU occupancy while
maintaining statistical correctness.
To demonstrate the applicability of these techniques, we present results from two Julia
packages developed for plasma and high-energy physics applications. The first,
QEDevents.jl, focuses on Monte-Carlo event generation for high-multiplicity
scattering processes in quantum electrodynamics, building on the generic sampling
framework RejectionSamplers.jl and the QuantumElectrodynamics.jl ecosystem. The second,
PhotonTransport.jl implements a Monte-Carlo photon transport code for warm-dense matter.
Together, these case studies illustrate how Julia enables both the expressive
implementation of parallel sampling algorithms and near-peak accelerator performance,
while maintaining portability across hardware backends.