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Use smarty markdown plugin for proper em-dashes etc. (#258)
See https://python-markdown.github.io/extensions/smarty/ for details. As far as I understand, specific replacements can be disabled if needed. Double and triple hyphens in code blocks are of course not changed.
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docs/alps/hardware.md

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@@ -100,7 +100,7 @@ These nodes have two [AMD Epyc 7742](https://en.wikichip.org/wiki/amd/epyc/7742)
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!!! note "Not all memory is available"
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The total memory available to jobs on the nodes is roughly 245 GB and 497 GB on the standard and large memory nodes respectively.
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The amount of memory available to your job also depends on the number of MPI ranks per node -- each MPI rank has a memory overhead.
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The amount of memory available to your job also depends on the number of MPI ranks per node---each MPI rank has a memory overhead.
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A schematic of a *standard memory node* below illustrates the CPU cores and [NUMA nodes](https://www.kernel.org/doc/html/v4.18/vm/numa.html).(1)
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{.annotate}
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* 512 GB DDR4 Memory
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* 4 NVIDIA A100 GPUs with 80 GB HBM3 memory each
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* The MCH system is the same, except the A100 have 96 GB of memory.
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* 4 NICs -- one per GPU.
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* 4 NICs---one per GPU.
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[](){#ref-alps-mi200-node}
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### AMD MI250x GPU Nodes

docs/platforms/hpcp/index.md

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@@ -67,7 +67,7 @@ The Store (or Project) file system is provided as a space to store datasets, cod
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The environment variable `$STORE` can be used as a shortcut to access the Store folder of your primary project.
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Hard limits on the amount of data and number of files (inodes) will prevent you from writing to [Store][ref-storage-store] if your quotas are exceeded.
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You can check how much data and inodes you are consuming -- and their respective quotas -- by running the [`quota`][ref-storage-quota] command on a login node.
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You can check how much data and inodes you are consuming---and their respective quotas---by running the [`quota`][ref-storage-quota] command on a login node.
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!!! warning
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It is not recommended to write directly to the `$STORE` path from batch jobs.

docs/running/slurm.md

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@@ -119,7 +119,7 @@ The following sections will document how to use Slurm on different compute nodes
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To demonstrate the effects different Slurm parameters, we will use a little command line tool [affinity](https://github.com/bcumming/affinity) that prints the CPU cores and GPUs that are assigned to each MPI rank in a job, and which node they are run on.
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We strongly recommend using a tool like affinity to understand and test the Slurm configuration for jobs, because the behavior of Slurm is highly dependent on the system configuration.
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Parameters that worked on a different cluster -- or with a different Slurm version or configuration on the same cluster -- are not guaranteed to give the same results.
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Parameters that worked on a different cluster---or with a different Slurm version or configuration on the same cluster---are not guaranteed to give the same results.
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It is straightforward to build the affinity tool to experiment with Slurm configurations.
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### OpenMP
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In the above examples all threads on each -- we are effectively allowing the OS to schedule the threads on the available set of cores as it sees fit.
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In the above examples all threads on each---we are effectively allowing the OS to schedule the threads on the available set of cores as it sees fit.
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This often gives the best performance, however sometimes it is beneficial to bind threads to explicit cores.
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The OpenMP threading runtime provides additional options for controlling the pinning of threads to the cores assigned to each MPI rank.
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Next, launch your applications using `srun`, carefully subdividing resources for each job step.
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The `--exclusive` flag must be used again, but note that its meaning differs in the context of `srun`.
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Here, `--exclusive` ensures that only the resources explicitly requested for a given job step are reserved and allocated to it.
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Without this flag, Slurm reserves all resources for the job step, even if it only allocates a subset -- effectively blocking further parallel `srun` invocations from accessing unrequested but needed resources.
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Without this flag, Slurm reserves all resources for the job step, even if it only allocates a subset---effectively blocking further parallel `srun` invocations from accessing unrequested but needed resources.
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Be sure to background each `srun` command with `&`, so that subsequent job steps start immediately without waiting for previous ones to finish.
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A final `wait` command ensures that your submission script does not exit until all job steps complete.

docs/software/cw/wrf.md

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@@ -35,7 +35,7 @@ The [`prgenv-gnu`][ref-uenv-prgenv-gnu] uenv is suitable for building WRF.
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```
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uenv start prgenv-gnu/24.11:v2 --view=spack
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```
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In this example we use the latest version of `prgenv-gnu` on Eiger at the time of writing -- check the `prgenv-gnu` [guide][ref-uenv-prgenv-gnu] for the latest version.
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In this example we use the latest version of `prgenv-gnu` on Eiger at the time of writing---check the `prgenv-gnu` [guide][ref-uenv-prgenv-gnu] for the latest version.
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```bash
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# build the latest version provided by the version of Spack used by prgenv-gnu

mkdocs.yml

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markdown_extensions:
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- admonition
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- def_list
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- markdown.extensions.smarty
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- pymdownx.details
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- pymdownx.superfences
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- pymdownx.superfences:

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