Multi-scale high-performance computing in astrophysics: simulating clusters with stars, binaries and planets

Abstract

<jats:p>The demand on simulation software in astrophysics has dramatically increased over the last decades. This increase is driven by improvements in observational data and computer hardware. At the same time, computers have become more complicated to program due to the introduction of more parallelism and hybrid hardware. To keep up with these developments, much of the software has to be redesigned. In order to prevent the future need to rewrite again when new developments present themselves, the main effort should go into making the software maintainable, flexible and scalable. In this paper, we explain our strategy for coupling elementary solvers and how to combine them into a high-performance multi-scale environment in which complex simulations can be performed. The elementary parts can remain succinct while supporting the aggregation to more satisfactory functionality by coupling them on a higher level. The advanced code-coupling strategies we present here allow such a hierarchy and support the development of complex codes. A library of simple elementary solvers subsequently stimulates the rapid development of more complex code that can co-evolve with the latest advances in computer hardware. We demonstrate how to combine several of these elementary solvers in a hierarchical and generic system, and how the resulting complex codes can be applied to multi-scale problems in astrophysics. Our aim is to achieve the best of several worlds with respect to performance, flexibility and maintainability while reducing development time. We succeeded in the development of the hierarchical coupling strategy and the general framework, but a comprehensive library of minimal fundamental-physics solvers is still unavailable.</jats:p> <jats:p>This article is part of the theme issue ‘Multiscale modelling, simulation and computing: from the desktop to the exascale’.</jats:p>

The Origin of the Stellar Mass Distribution and Multiplicity

“…However, statistical significance is even harder to achieve in this approach. More efficient algorithms, new methods exploiting GPU capabilities and the application of machine learning to model statistical processes like dynamical interactions show promise for future progress (Schive et al, 2018;Nordlund et al, 2018;van Elteren et al, 2019).…”

Multiscale modelling, simulation and computing: from the desktop to the exascale

“…Neumann et al [13] and van Elteren et al [14] introduce two challenging application domains, weather/climate and astrophysics, respectively, discuss the complex multiscale nature of problems addressed in those domains, and demonstrate if and how multiscale computing towards exascale performance would be able to provoke next big steps in these fields. Another equally complex and relevant field with potential impact, which could benefit tremendously from advanced multiscale computing, is that of soft flowing crystals.…”