Victor Artigues | Searcharxiv

arXiv2026

iGENE: A Differentiable Flux-Tube Gyrokinetic Code in TensorFlow

We present iGENE, a fully-differentiable TensorFlow implementation of the electromagnetic local nonlinear gyrokinetic model, which allows us to compute gradients of any simulation output with respect to any input via automatic differentiation. We show that even if the stochastic nature of turbulence prevents the exact evaluation of gradients of nonlinear quantities of interest, they can still be successfully used to perform outer-loop tasks, such as profile predictions. This work enables the integration of gyrokinetics into automated parameter optimization, uncertainty quantification, sensitivity analysis, and AI workflows.

physics.plasm-ph↗

arXiv2019

Evaluation of performance portability frameworks for the implementation of a particle-in-cell code

This paper reports on an in-depth evaluation of the performance portability frameworks Kokkos and RAJA with respect to their suitability for the implementation of complex particle-in-cell (PIC) simulation codes, extending previous studies based on codes from other domains. At the example of a particle-in-cell model, we implemented the hotspot of the code in C++ and parallelized it using OpenMP, OpenACC, CUDA, Kokkos, and RAJA, targeting multi-core (CPU) and graphics (GPU) processors. Both, Kokkos and RAJA appear mature, are usable for complex codes, and keep their promise to provide performance portability across different architectures. Comparing the obtainable performance on state-of-the art hardware, but also considering aspects such as code complexity, feature availability, and overall productivity, we finally draw the conclusion that the Kokkos framework would be suited best to tackle the massively parallel implementation of the full PIC model.

cs.DC↗