Static load balancing for molecular-continuum flow simulations with heterogeneous particle systems and on heterogeneous hardware
Publication date
2025-07-05
Document type
Konferenzbeitrag
Author
Organisational unit
Conference
25th International Conference on Computational Science (ICCS 2025) ; Singapore ; July 7–9, 2025
Publisher
Springer
Series or journal
Lecture Notes in Computer Science
Periodical volume
15905
Book title
Computational Science – ICCS 2025
Peer-reviewed
✅
Part of the university bibliography
✅
Language
English
Keyword
dtec.bw
Abstract
Load balancing in particle simulations is a well-researched field, but its effect on molecular-continuum coupled simulations is comparatively less explored.
In this work, we implement static load balancing into the macro-micro-coupling tool (MaMiCo), a software for molecular-continuum coupling, and demonstrate its effectiveness in two classes of experiments by coupling with the particle simulation software ls1 mardyn. The first class comprises a liquid-vapour multiphase scenario, modelling evaporation of a liquid into vacuum and requiring load balancing due to heterogeneous particle distributions in space. The second class considers execution of molecular-continuum simulations on heterogeneous hardware, running at very different efficiencies. After a series of experiments with balanced and unbalanced setups, we find that, with our balanced configurations, we achieve a reduction in runtime by 44% and 55% respectively.
In this work, we implement static load balancing into the macro-micro-coupling tool (MaMiCo), a software for molecular-continuum coupling, and demonstrate its effectiveness in two classes of experiments by coupling with the particle simulation software ls1 mardyn. The first class comprises a liquid-vapour multiphase scenario, modelling evaporation of a liquid into vacuum and requiring load balancing due to heterogeneous particle distributions in space. The second class considers execution of molecular-continuum simulations on heterogeneous hardware, running at very different efficiencies. After a series of experiments with balanced and unbalanced setups, we find that, with our balanced configurations, we achieve a reduction in runtime by 44% and 55% respectively.
Version
Published version
Access right on openHSU
Metadata only access