Numerical simulations are essential to our understanding of star and planet formation. They imply processes being multi-physics, complex, multi-scale, out of equilibrium, and non linear. Recently, the computing power of supercomputer in- creased up to the exascale, namely a quintillion operations per seconds. In principle, this computing power makes it possible to resolve crucial questions about planet formation, thanks to simulations of unprecedented accuracy. To achieve this, it is necessary to develop code based on algorithms capable of taking advantage of this new computing power.
The aim of this thesis is to develop Shamrock, the first astrophysical code with exascale multi-methods (particles or adaptive grids). The core of this work is the adaptation and optimization of a binary algorithm for finding randomly distributed neighbors, which is fully parallelizable on architectures using graphics cards. In its current version, Shamrock achieves a parallel efficiency of over 90% for a Sedov test performed with the Smoothed Particle Hydrodynamics (SPH) method on 1024 nodes, enabling the first simulations with 65 billion particles to be carried out in 7 seconds per time step.
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