Clariden docs

docs/tools/slurm.md

@@ -9,16 +9,25 @@ SLURM is an open-source, highly scalable job scheduler that allocates computing
 !!! todo
     document `--account`, `--constrant` and other generic flags.
 
+[](){#ref-slurm-partitions}
 ## Partitions
 
 At CSCS, SLURM is configured to accommodate the diverse range of node types available in our HPC clusters. These nodes vary in architecture, including CPU-only nodes and nodes equipped with different types of GPUs. Because of this heterogeneity, SLURM must be tailored to ensure efficient resource allocation, job scheduling, and workload management specific to each node type.
 
 Each type of node has different resource constraints and capabilities, which SLURM takes into account when scheduling jobs. For example, CPU-only nodes may have configurations optimized for multi-threaded CPU workloads, while GPU nodes require additional parameters to allocate GPU resources efficiently. SLURM ensures that user jobs request and receive the appropriate resources while preventing conflicts or inefficient utilization.
 
+[](){#ref-slurm-partition-debug}
+### Debug partition
+The SLURM `debug` partition is useful for quick turnaround workflows. The partition has a short maximum time (timelimit can be seen with `sinfo -p debug`), and a low number of maximum nodes (the `MaxNodes` can be seen with `scontrol show partition=debug`).
+
+[](){#ref-slurm-partition-normal}
+### Normal partition
+This is the default partition, and will be used when you do not explicitly set a partition. This is the correct choice for standard jobs. The maximum time is usually set to 24 hours (`sinfo -p normal` for timelimit), and the maximum nodes can be as much as nodes are available.
+
 The following sections will provide detailed guidance on how to use SLURM to request and manage CPU cores, memory, and GPUs in jobs. These instructions will help users optimize their workload execution and ensure efficient use of CSCS computing resources.
 
 [](){#ref-slurm-gh200}
-### NVIDIA GH200 GPU Nodes
+## NVIDIA GH200 GPU Nodes
 
 The [GH200 nodes on Alps][ref-alps-gh200-node] have four GPUs per node, and SLURM job submissions must be configured appropriately to best make use of the resources.
 Applications that can saturate the GPUs with a single process per GPU should generally prefer this mode.
@@ -40,7 +49,7 @@ See [Scientific Applications][ref-software-sciapps] for information about recomm
     If the variable is unset or empty all GPUs are visible to the rank and the rank will in most cases only use the first GPU. 
 
 [](){#ref-slurm-gh200-single-rank-per-gpu}
-#### One rank per GPU
+### One rank per GPU
 
 Configuring SLURM to use one GH200 GPU per rank is easiest done using the `--ntasks-per-node=4` and `--gpus-per-task=1` SLURM flags.
 For advanced users, using `--gpus-per-task` is equivalent to setting `CUDA_VISIBLE_DEVICES` to `SLURM_LOCALID`, assuming the job is using four ranks per node.
@@ -59,7 +68,7 @@ srun <application>
 Omitting the `--gpus-per-task` results in `CUDA_VISIBLE_DEVICES` being unset, which will lead to most applications using the first GPU on all ranks.
 
 [](){#ref-slurm-gh200-multi-rank-per-gpu}
-#### Multiple ranks per GPU
+### Multiple ranks per GPU
 
 Using multiple ranks per GPU can improve performance e.g. of applications that don't generate enough work for a GPU using a single rank, or ones that scale badly to all 72 cores of the Grace CPU.
 In these cases SLURM jobs must be configured to assign multiple ranks to a single GPU.

docs/vclusters/clariden.md

@@ -6,7 +6,11 @@
 
     This page is a cut and paste of some of Todi's old documentation, which we can turn into a template.
 
-## Cluster Details
+## Cluster Specification
+### Hardware
+Clariden consists of ~1200 [Grace-Hopper nodes][ref-alps-gh200-node]. Most nodes are in the [`normal` slurm partition][ref-slurm-partition-normal], while a few nodes are in the [`debug` partition][ref-slurm-partition-debug].
+
+
 
 !!! todo
     a standardised table with information about