This guide provides an introduction to the SLURM job scheduler and its application on the c2b2 clusters. The primary, general purpose compute cluster at C2B2 is now called "Ganesha.", This HPC cluster s a Linux-based (Rocky9.4) compute cluster consisting of 62 Dell Server 2 head nodes and a virtualized pool of login (submit) nodes 8 Weka nodes. The nodes fit in a dense configuration in 9 high-density racks and are cooled by dedicated rack refrigeration systems.
The clusters comprise:
8 20 compute nodes, each with 20-192 core processors and 128 768 GB of memory
Some 2 nodes have with 192 cores and 1.5 TB of memory
1 40 GPU node featuring 2 NVIDIA L40s GPU cards 192 cores processors and 768 GB memory
1 GPU node with a Superchip GH200 ARM architecture, 1 GPU, and 570 GB of memory
This guide will help you get started with using SLURM on these clusters.
Introductions
Jobs are executed in batch mode, without user intervention. The typical process involves
Logging into the login node ((link unavailable))
Preparing a job script that specifies the work to be done and required computing resources
Submitting the job to the queue
Optionally logging out while the job runs
Returning to collect the output data once the job is complete
...
Essential SLURM commands
Creating a job submission script
Understanding SLURM partitions
Submitting jobs to the queue
Monitoring job progress
Canceling jobs from the queue
Setting environment variables
Managing job dependencies and job arrays
Commands
The following table summarizes the most commonly used SLURM commands:
...
Command
...
Description
...
sbatch
...
Submits a job script for execution.
...
sinfo
...
Displays the status of SLURM-managed partitions and nodes, with customizable filtering, sorting, and formatting options.
...
squeue
...
Shows the status of jobs, with options for filtering, sorting, and formatting, defaulting to priority order for running and pending jobs.
...
srun
...
Run a parallel job on cluster.
...
scancel
...
Cancels a pending or running job
...
sacct
...
Provides accounting information for active or completed jobs.
...
salloc
...
This command is used to allocate resources and submit an interactive job to Slurm, allowing users to execute tasks in real-time with manual input.
SLURM commands offer detailed documentation and guidance through their manual (man) pages, which can be accessed by typing, for example
|
|---|
Submission script
A submission script is a shell script that outlines the computing tasks to be performed, including the application, input/output, and resource requirements (e.g., CPUs, memory). A basic example is a job that needs a single node with the following specifications:
Uses 1 node
Runs a single-process application
Has a maximum runtime of 100 hours
Is named "MyHelloBatch"
Sends email notifications to the user when the job starts, stops, or aborts"
Example 1: job running on single node
| Code Block | ||
|---|---|---|
| ||
#!/bin/bash
#MyHelloBatch.slurm
#
#SBATCH -J test # Job name, any string
#SBATCH -o job.%j.out # Name of stdout output file (%j=jobId)
#SBATCH -N 1 # Total number of nodes requested
#SBATCH -n 8 # Total number of cpu requested
#SBATCH -t 01:30:00 # Run time (hh:mm:ss) - 1.5 hours
#SBATCH --mail-user=UNI@cumc.columbia.edu # use only Columbia address
#SBATCH --mail-type=ALL # send email alert on all events
module load anaconda/3.0 # load the appropriate module(s) needed by
python hello.py # you program |
A submission script begins with #!/bin/bash, indicating it's a Linux bash script. Comments start with #, while #SBATCH lines specify job scheduling resources for SLURM. Note that #SBATCH directives must be placed at the top of the script, before any other commands. The script requests resources, such as:
#SBATCH -N n or #SBATCH --nodes=n : specifies the number of compute nodes (only 1 in this case)
#SBATCH -t T or #SBATCH --time=T: sets the maximum walltime (hh:mm:ss format)
#SBATCH -J “name" or #SBATCH --job-name="name": assigns a job name
#SBATCH --mail-user=<email_address>: sends email notifications
#SBATCH --mail-type=<type>: sets notification options (BEGIN, END, FAIL, REQUEUE, or ALL)
The script's final section is a standard Linux bash script, outlining job operations. By default, the job starts in the submission folder with the same environment variables as the user. In this example, the script simply runs the python hello.py.
Example 2: job running on multiple nodes
To execute an MPI application across multiple nodes, we need to modify the submission script to request additional resources and specify the MPI execution command:
| Code Block |
|---|
#!/bin/bash
#MyHelloBatch.slurm
#
#SBATCH -J test # Job name, any string
#SBATCH -o job.%j.out # Name of stdout output file (%j=jobId)
#SBATCH -N 2 # Total number of nodes requested
#SBATCH --ntasks-per-node=16 # set the number of tasks (processes) per node
#SBATCH -t 01:30:00 # Run time (hh:mm:ss) - 1.5 hours
#SBATCH -p highmem # Queue name. Specify gpu for the GPU node.
#SBATCH --mail-user=UNI@cumc.columbia.edu # use only Columbia address
#SBATCH --mail-type=ALL # send email alert on all events
module load openmpi4/4.1.1 # load the appropriate module(s) needed by
mpirun myMPICode # you program |
The multi-node script is similar to the single-node one, with the key addition of #SBATCH --ntasks-per-node=m to reserve cores and enable MPI parallel processing.Each node has a 25 Gbps ethernet connection and 100 Gbps HDR InfiniBand. Additionally, a set of login nodes running on Proxmox virtualization provide a pool of virtual login nodes for user access to this and other systems.
Like our previous clusters, this cluster the system is controlled by SLURM. Storage for the cluster is provided exclusively by our Weka parallel filesystem with over 1 PB of total capacity.
For assistance with cluster-related issues, please email dsbit-help@cumc.columbia.edu, including the following details in your message:
Your Columbia University Network ID (UNI)
Job ID numbers, if your inquiry pertains to a specific job issue
This information will help ensure a prompt and accurate response to your cluster-related questions.