The different parts of the batch system¶

The batch system is made up of several different types of resources. At the top we have partitions, which are similar to queues. Each partition is made up of a specific set of nodes. At HPC2N we have three classes of partitions, one for CPU-only nodes, one for GPU nodes and one for large memory nodes. Each node type also has a set of features that can be used to select which node(s) the job should run on.

The three types of nodes also have corresponding resources one must apply for in SUPR to be able to use them.

Partitions¶

There is only a single partition, batch, that users can submit jobs to. The system then figures out, based on requested features which actual partition(s) the job should be sent to.

(Kebnekaise has multiple partitions, one for each major type of resource.)

The command to ask for a specific partition in your Slurm submit script is:

#SBATCH -p PARTITION

where PARTITION is the name of the partition. If no partition is requested, the default, batch will be used.

Information about the Slurm nodes and partitions can be found using this command:

$ sinfo

Unless otherwise specified the system will allocate resources on the first available node(s) that the job can run on. It will try to use as few resources as possible, i.e. use nodes with less memory per core before nodes with more memory etc.

Nodes¶

Kebnekaise have CPU-only, GPU enabled and large memory nodes.

The CPU-only nodes are:¶

• 2 x 14 core Intel skylake
  • 6785 MB memory / core
  • 48 nodes
• 2 x 64 core AMD zen3
  • 8020 MB / core
  • 1 node
• 2 x 128 core AMD zen4
  • 2516 MB / core
  • 8 nodes

To request node(s) with a specific type of CPU use the available features

The GPU enabled nodes are:¶

• 2 x 14 core Intel skylake
  • 6785 MB memory / core
  • 2 x Nvidia V100
  • 10 nodes
• 2 x 24 core AMD zen3
  • 10600 MB / core
  • 2 x Nvidia A100
  • 2 nodes
• 2 x 24 core AMD zen3
  • 10600 MB / core
  • 2 x AMD MI100
  • 1 node
• 2 x 24 core AMD zen4
  • 6630 MB / core
  • 2 x Nvidia A6000
  • 1 node
• 2 x 24 core AMD zen4
  • 6630 MB / core
  • 2 x Nvidia L40s
  • 10 nodes
• 2 x 48 core AMD zen4
  • 6630 MB / core
  • 4 x Nvidia H100 SXM5
  • 2 nodes
• 2 x 32 core AMD zen4
  • 11968 MB / core
  • 6 x Nvidia L40s
  • 2 nodes
  • Can only use 10 cores/GPU
• 2 x 32 core AMD zen4
  • 11968 MB / core
  • 8 x Nvidia A40
  • 2 nodes

See the next section regarding requesting GPUs.

The large memory nodes are:¶

• 4 x 18 core Intel broadwell
  • 41666 MB memory / core
  • 8 nodes

Requesting GPUs¶

To use GPU resources one has to explicitly ask for one or more GPUs. Requests for GPUs can be done either in total for the job or per node of the job.

#SBATCH --gpus=1

#SBATCH --gpus-per-node=1

One can ask for a specific GPU type by using

#SBATCH --gpus=l40s:1

To request node(s) with a specific type of GPU use the available features or the generic GPU features.

Requesting specific features (i.e. setting contraints on the job)¶

To make it possible to target nodes in more detail there are a couple of features defined on each group of nodes. To select a feature one can use the -C option to sbatch or salloc

I.e. to constrain the job to nodes with the zen4 feature use:

#SBATCH -C zen4

Features can be combined using “and” or “or” like this:

#SBATCH -C 'zen4&GPU_AI'

#SBATCH -C 'zen3|zen4'

We have at least the following features defined depending on type of node.

For selecting type of CPU:¶

• intel_cpu
• broadwell
• skylake
• amd_cpu
• zen3
• zen4

For selecting type of GPU:¶

• v100
• a40
• a6000
• a100
• l40s
• h100
• mi100

The above list can be used either as a specifier to –gpu=type:number or as a constraint together with an unspecified gpu request –gpu=number

For selecting GPUs with certain features:¶

• nvidia_gpu (Any Nvidia GPU)
• amd_gpu (Any AMD GPU)
• GPU_SP (GPU with single precision capability)
• GPU_DP (GPU with double precision capability)
• GPU_AI (GPU with AI features, like half precisions and lower)
• GPU_ML (GPU with ML features, like half precisions and lower)

For selecting large memory nodes:¶

• largemem

Useful environment variables in a job¶

These are a few of the important and most useful environment variables available in a job.

• SLURM_JOB_NUM_NODES: the number of nodes you got.
• SLURM_NTASKS: contains the number of task slots allocated. Note that this is only set if one of the –ntasks options was used.
• SLURM_CPUS_PER_TASK: the number of cores allocated per task. Note that this is only set if the -c option was used.
• SLURM_CPUS_ON_NODE: the total number of cores allocated on the node for the job.
• SLURM_JOB_ID: contains the id of the current job.