Helix/Slurm
General information about Slurm
The bwForCluster Helix uses Slurm as batch system.
- Slurm documentation: https://slurm.schedmd.com/documentation.html
- Slurm cheat sheet: https://slurm.schedmd.com/pdfs/summary.pdf
- Slurm tutorials: https://slurm.schedmd.com/tutorials.html
Slurm Command Overview
| Slurm commands | Brief explanation |
|---|---|
| sbatch | Submits a job and queues it in an input queue |
| saclloc | Request resources for an interactive job |
| squeue | Displays information about active, eligible, blocked, and/or recently completed jobs |
| scontrol | Displays detailed job state information |
| scancel | Cancels a job |
Job Submission
Batch jobs are submitted with the command:
$ sbatch <job-script>
A job script contains options for Slurm in lines beginning with #SBATCH as well as your commands which you want to execute on the compute nodes. For example:
#!/bin/bash
#SBATCH --partition=single
#SBATCH --ntasks=1
#SBATCH --time=00:20:00
#SBATCH --mem=1gb
#SBATCH --export=NONE
echo 'Hello world'
This jobs requests one core (--ntasks=1) and 1 GB memory (--mem=1gb) for 20 minutes (--time=00:20:00) on nodes provided by the partition 'single'.
For the sake of a better reproducibility of jobs it is recommended to use the option --export=NONE to prevent the propagation of environment variables from the submit session into the job environment and to load required software modules in the job script.
Partitions
On bwForCluster Helix it is necessary to request a partition with '--partition=<partition_name>' on job submission. Within a partition job allocations are routed automatically to the most suitable compute node(s) for the requested resources (e.g. amount of nodes and cores, memory, number of GPUs). The devel partition is the default partition, if no partition is requested.
The partitions devel and single are operated in shared mode, i.e. jobs from different users can run on the same node. Jobs can get exclusive access to compute nodes in these partitions with the "--exclusive" option. The partitions cpu-multi and gpu-multi are operated in exclusive mode. Jobs in these partitions automatically get exclusive access to the requested compute nodes.
GPUs are requested with the option "--gres=gpu:<number-of-gpus>".
| Partition | Node Access Policy | Node Types | Default | Limits |
|---|---|---|---|---|
| devel | shared | cpu, gpu4 | ntasks=1, time=00:10:00, mem-per-cpu=2gb | nodes=2, time=00:30:00 |
| single | shared | cpu, fat, gpu4, gpu8 | ntasks=1, time=00:30:00, mem-per-cpu=2gb | nodes=1, time=120:00:00 |
| cpu-multi | job exclusive | cpu | nodes=2, time=00:30:00 | nodes=32, time=48:00:00 |
| gpu-multi | job exclusive | gpu4 | nodes=2, time=00:30:00 | nodes=8, time=48:00:00 |
Constraints
It is possible to request explicitly the CPU manufacturer of compute nodes with the option "--constraint=<constraint_name>".
| Constraint | Meaning |
|---|---|
| amd | request AMD nodes (default) |
| intel | request Intel nodes (when available) |
Examples
Here you can find some examples for resource requests in batch jobs.
Serial Programs
#SBATCH --partition=single
#SBATCH --ntasks=1
#SBATCH --time=120:00:00
#SBATCH --mem=4gb
Notes:
- Jobs with "--mem" below 240gb can run on all node types associated with the single partition.
Multi-threaded Programs
#SBATCH --partition=single
#SBATCH --nodes=1
#SBATCH --ntasks-per-node=16
#SBATCH --time=01:30:00
#SBATCH --mem=50gb
Notes:
- Jobs with "--ntasks-per-node" up to 64 and "--mem" below 240gb can run on all node types associated with the single partition.
MPI Programs
#SBATCH --partition=cpu-multi
#SBATCH --nodes=2
#SBATCH --ntasks-per-node=64
#SBATCH --time=12:00:00
#SBATCH --mem=50gb
Notes:
- "--mem" requests the memory per node. Jobs with "--mem" below 240gb can run on all node types associated with the cpu-multi partition.
GPU Programs
#SBATCH --partition=single
#SBATCH --nodes=1
#SBATCH --ntasks-per-node=40
#SBATCH --gres=gpu:4
#SBATCH --time=12:00:00
#SBATCH --mem=200gb
Notes:
- The number of GPUs per node is requested with the option "--gres=gpu:<number-of-gpus>"
- It is possible to request a certain GPU type with the option "--gres=gpu:<gpu-type>:<number-of-gpus>". For <gpu-type> put the 'GPU Type' listed in the last line of the GPU Nodes Hardware table.
Interactive Jobs
Interactive jobs must NOT run on the logins nodes, however resources for interactive jobs can be requested using srun. The following example requests an interactive session on 1 core for 2 hours:
$ salloc --partition=single --ntasks=1 --time=2:00:00
After execution of this command wait until the queueing system has granted you the requested resources. Once granted you will be automatically logged on the allocated compute node.
If you use applications or tools which provide a GUI, enable X-forwarding for your interactive session with:
$ salloc --partition=single --ntasks=1 --time=2:00:00 --x11
Once the walltime limit has been reached you will be automatically logged out from the compute node.
Job Monitoring
Information about submitted jobs
For an overview of your submitted jobs use the command:
$ squeue
To get detailed information about a specific jobs use the command:
$ scontrol show job <jobid>
Interactive access to running jobs
If you like to see what happens on the compute node(s), you can access the allocated resources of a running job with:
$ srun --jobid=[jobid] --pty /bin/bash
Commands like 'top' show you the most busy processes on the node. To exit 'top' type 'q'.
To monitor your GPU processes use the command 'nvidia-smi'.
In the case of multi node jobs, lookup the node names of your job with squeue and add one of the node names with the --nodelist option to the srun command:
$ srun --jobid=[jobid] --nodelist=[node-name] --pty /bin/bash
Job Feedback
You can find feedback on resource usage and job efficiency at the end of the regular output file.
Example Output:
============================= JOB FEEDBACK =============================
Job ID: 12345678
Cluster: helix
User/Group: hd_ab123/hd_hd
State: COMPLETED (exit code 0)
Nodes: 2
Cores per node: 32
CPU Utilized: 06:20:04
CPU Efficiency: 49.90% of 12:41:36 core-walltime
Job Wall-clock time: 00:11:54
Memory Utilized: 41.06 GB
Memory Efficiency: 32.68% of 125.65 GB
Explanation:
- Nodes: Number of allocated nodes for the job.
- Cores per node: Number of physical cores per node allocated for the job.
- CPU Utilized: Sum of utilized core time.
- CPU Efficiency: 'CPU Utilized' with respect to core-walltime (= 'Nodes' x 'Cores per node' x 'Job Wall-clock time') in percent.
- Job Wall-clock time: runtime of the job.
- Memory Utilized: Sum of memory used.
- Memory Efficiency: 'Memory Utilized' with respect to total allocated memory for the job.
Accounting
Overview about free resources
On the login nodes the following command shows what resources are available for immediate use:
$ sinfo_t_idle