Title: AutoPas in ls1 mardyn: Massively parallel particle simulations with node-level auto-tuning
Authors: Seckler, Steffen
Gratl, Fabio
Heinen, Matthias
Vrabec, Jadran
Bungartz, Hans Joachim
Neumann, Philipp 
Language: en
Subject (DDC): DDC::500 Naturwissenschaften und Mathematik
Issue Date: Mar-2021
Document Type: Article
Project: Task-basierte Lastverteilung und Auto-Tuning in der Partikelsimulation
Journal / Series / Working Paper (HSU): Journal of computational science 
Volume: 50
Due to computational cost, simulation software is confronted with the need to always use optimal building blocks — data structures, solver algorithms, parallelization schemes, and so forth — in terms of efficiency, while it typically needs to support a variety of hardware architectures. AutoPas implements the computationally most expensive molecular dynamics (MD) steps (e.g., force calculation) and chooses on-the-fly, i.e., at run time, the optimal combination of the previously mentioned building blocks. We detail decisions made in AutoPas to enable the interplay with MPI-parallel simulations and, to our knowledge, showcase the first MPI-parallel MD simulations that use dynamic tuning. We discuss the benefits of this approach for three simulation scenarios from process engineering, in which we obtain performance improvements of up to 50%, compared to the baseline performance of the highly optimized ls1 mardyn software.
Organization Units (connected with the publication): High Performance Computing 
URL: https://api.elsevier.com/content/abstract/scopus_id/85099677878
ISSN: 18777503
DOI: 10.1016/j.jocs.2020.101296
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