
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
| 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). | 0 | |
| 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. | Average | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
