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@@ -44,8 +44,10 @@ systems. It also comes with plenty of analysis scripts.
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| Version | Available modules | Notes |
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|:-------:|:------------------|:-----:|
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|2022.5 |`gromacs/2022.5`<br>`gromacs/2022.5-plumed`|Module with Plumed available
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|2023 |`gromacs/2023-dev-rocm`|Unofficial GPU-enabled fork developed by AMD
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|2023.1 |`gromacs/2023.1`<br>`gromacs/2023.1-hipsycl`|GPU-enabled module available
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|2023 |`gromacs/2023-dev-rocm`|Unofficial GPU-enabled fork developed by AMD[^1]
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|2023.1 |`gromacs/2023.1`<br>`gromacs/2023.1-hipsycl`<br>`gromacs/2023.1-heffte`|Official GPU-enabled modules available (hipSYCL)<br>Module linked to HeFFTe available for GPU PME decomposition
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[^1]: This module is unvalidated, unmaintained and unsupported by the Gromacs team. Proceed with caution!
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- Puhti and Mahti have also `gromacs-env/<year>` modules for loading the recommended
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latest minor version from each year (replace `<year>` accordingly).
@@ -329,6 +331,21 @@ node. Importantly, the amount of GPU nodes on LUMI is massively larger than on M
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feature of Gromacs. For more details about GPU-sharing and aggregate sampling, see our
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[tutorial on high-throughput simulations with Gromacs](../support/tutorials/gromacs-throughput.md).
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#### PME decomposition
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Scaling of huge systems with several millions atoms may be limited by single GPU PME. To
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significantly improve the scaling, decomposition of PME to multiple GPUs is possible in the
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`gromacs/2023.1-heffte` module with the [HeFFTe library](https://icl-utk-edu.github.io/heffte/)
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linked. Add the following exports to the batch script above:
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```bash
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export GMX_GPU_PME_DECOMPOSITION=1
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export GMX_PMEONEDD=1
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```
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The number of PME ranks to use depends on the specific case, but 1 or 2 per GPU *node* should
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be a reasonable starting point. So for 16 LUMI-G nodes, use `-npme 16` or `-npme 32`.
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### Visualizing trajectories and graphs
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In addition to the `view` tool of Gromacs (not available at CSC),
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