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arXiv · 2607.25866

Massively parallel numerical simulations with Julia

Simon Candelaresi·Benedict Geihe·Marco Artiano·Lars Christmann·Valentin Churavy·Andrés Rueda-Ramírez·Hendrik Ranocha·Gregor J. Gassner·Michael Schlottke-Lakemper
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Abstract

The Julia programming language aims to provide a modern approach to develop high-performance computing (HPC) applications. It tries to achieve this by combining a high-level, dynamic interface with just-in-time compilation to native machine code, thereby facilitating high developer productivity and native code performance at the same time. While this approach has already been shown to work well for serial applications, it is not clear if it readily translates to traditional, massively parallel HPC work loads. In this paper, we fill this gap by analyzing the parallel performance of the numerical computational fluid dynamics simulation code Trixi$.$jl, written in Julia, and compare it to the Fortran code FLUXO. We show some of the challenges of using Julia at scale and discuss possible solutions, specifically with respect to code loading and compilation at startup. Finally, we demonstrate the parallel scaling of our Julia code on up to 61440 CPU cores.

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Simon Candelaresi, Benedict Geihe, Marco Artiano, Lars Christmann, Valentin Churavy, Andrés Rueda-Ramírez, Hendrik Ranocha, Gregor J. Gassner, Michael Schlottke-Lakemper. 2026-07-28. Massively parallel numerical simulations with Julia. https://arxiv.org/abs/2607.25866

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