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Workshop: How to run GROMACS efficiently on the LUMI supercomputer

Authors: Páll, Szilárd; Alekseenko, Andrey; Villa, Alessandra; Kronberg, Rasmus; Raj, Xavier Anthony;

Workshop: How to run GROMACS efficiently on the LUMI supercomputer

Abstract

Lecture and hands-on material for the workshop How to run GROMACS efficiently on the LUMI supercomputer, 24 – 25 January 2024, online. The workshop gives practical tips on how to run GROMACS simulations efficiently on LUMI-G, i.e. on AMD GPUs. The participants learn how to assess and tune GROMACS performance. In addition the course provides an overview on LUMI architecture, GROMACS heterogeneous parallelization, with a special attention to AMD GPU. The event is organized by BioExcel in collaboration with CSC and PDC Topics LUMI architecture - Rasmus Kronberg (CSC) Brief Introduction to GROMACS - Alessandra Villa (KTH, Royal Institute of Technology) GROMACS parallelization / heterogeneous/GPU algorithms - Szilárd Páll (KTH, Royal Institute of Technology) AMD GPU support in GROMACS - Andrey Alekseenko (KTH, Royal Institute of Technology) How to run on LUMI - Rasmus Kronberg (CSC) Assessing and tuning performance of GROMACS simulations - Szilárd Páll (KTH, Royal Institute of Technology) Hands-on on GPU accelerated simulations, Scaling GROMACS across multiple GPUs, Ensemble parallelization across multiple GPUs (including input files for STMV and aquaporin system, and help-with-solution files) Batch scripts and reference log files for hands-on exercises can be downloaded here

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
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