Available batch job partitions
Note
The available LUMI batch job partitions are found in the LUMI documentation.
List all available partitions and their specifications on a system:
sinfo
Get details about a specific partition:
scontrol show partition <partition_name>
Puhti partitions
The following partitions (aka queues) are currently available in Puhti for normal (CPU) nodes:
Partition | Time limit |
Max tasks |
Max nodes |
Node types | Max memory |
Max local storage (NVMe) |
---|---|---|---|---|---|---|
test | 15 minutes | 80 | 2 | M | 185 GiB | |
interactive | 7 days | 8 | 1 | IO | 76 GiB | 720 GiB |
small | 3 days | 40 | 1 | M, L, IO | 373 GiB | 3600 GiB |
large | 3 days | 1040 | 26 | M, L, IO | 373 Gib | 3600 GiB |
longrun | 14 days | 40 | 1 | M, L, IO | 373 GiB | 3600 GiB |
hugemem | 3 days | 160 | 4 | XL, BM | 1496 GiB | |
hugemem_longrun | 14 days | 40 | 1 | XL, BM | 1496 GiB |
The following partitions are available on GPU nodes. Note that for each GPU, you should reserve at most 10 cores/tasks.
Partition | Time limit |
Max GPUs |
Max nodes |
Node types | Max memory |
Max local storage (NVMe) |
---|---|---|---|---|---|---|
gputest | 15 minutes | 8 | 2 | GPU | 373 GiB | 3600 GiB |
gpu | 3 days | 80 | 20 | GPU | 373 GiB | 3600 GiB |
Notes on partitions
- Use the
test
andgputest
partitions for testing code- The available resources are limited, but the queuing times are short
- Only use the
gputest
andgpu
partitions if you are sure your code uses GPUs- There are more CPU nodes than GPU nodes, so your job will run sooner if using CPUs
- Only request multiple CPU cores if you are sure your tool or code can
use them
- Reserving more cores does nothing by itself, except make you queue for longer
- Only request the memory you need
- Memory may become a bottleneck for resource requests; requesting less memory will decrease your time in queue
- Billing is based on memory requested, not on memory used
- Only use the
longrun
partitions if necessary- These partitions have stricter limits and a lower priority
- Only two jobs per user are allowed in the
interactive
partition- Use through apps in the web interface or with the command sinteractive
Mahti partitions
The following full-node partitions (aka queues) are currently available in Mahti on CPU nodes. When using these partitions, your jobs use all resources available on the node and won't share the node with other jobs.
Partition | Nodes | Time limit |
Access |
---|---|---|---|
test | 1-2 | 1 hours | all |
medium | 1-20 | 36 hours | all |
large | 20-200 | 36 hours | scalability test |
gc | 200-700 | 36 hours | Grand Challenge |
The following partition is available for allocation by resources in Mahti on CPU nodes. This means that you can request a sub-node allocation: you can request only part of the resources (cores and memory) available on the compute node. This also means that your job may share the node with other jobs. Each core reserved will provide 1.875 GB of memory and the only way to increase the memory reservation is to increase the number of cores reserved.
Partition | Cores | Time limit |
Max memory |
Access |
---|---|---|---|---|
interactive | 1-32 | 7 days | 60 GiB | all |
The following partitions are available on GPU nodes. Note that for each full A100 GPU, you can reserve at most 32 cores/tasks and for each GPU the job is allocated 122.5 GiB of memory. A subset of the A100 GPUs are sliced into smaller a100_1g.5gb GPUs with one seventh of the compute and memory capacity of a full A100 GPU. For each small GPU you can reserve at most 4 cores/tasks and for each GPU the job is allocated 17.5 GiB of memory. Also note that you can reserve at most one small GPU per job.
Partition | Time limit |
Max GPUs |
Max nodes |
Node types | Max local storage (NVMe) |
---|---|---|---|---|---|
gputest | 15 minutes | 4 | 1 | GPU | 3800 GiB |
gpusmall | 36 hours | 2 | 1 | GPU | 3800 GiB |
gpumedium | 36 hours | 24 | 6 | GPU | 3800 GiB |
Interactive partition, is intended for pre- and post processing tasks. It can be used both for interactive working and in batch jobs, where reserving a full node is not reasonable.
The basic CPU based HPC computing should be done in medium, large or gc partitions. Only full nodes are allocated from these partitions and the jobs will have access to all cores and memory in the nodes. You may choose to run with fewer cores if that gives better performance. In these partitions billing is based on the allocated nodes.
The large partition on Mahti is only accessible to projects that have done a scalability test and shown good utilization of the resources. The partition gc is only accessible to Grand Challenge projects and allows users to run extremely large simulations.