SLURM User Guide

Note: This guide provides an introduction to the SLURM job scheduler and its application on the c2b2 clusters. The clusters comprise:

• 8 compute nodes, each with 20-core processors and 128 GB of memory

• Some nodes have 192 cores and 1.5 TB of memory

• 1 GPU node featuring 2 NVIDIA L40s GPU cards

• 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

This guide provides an introduction to submitting and monitoring jobs using SLURM. The covered topics include:

• 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:

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: job running on a single node
  

  #!/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:

#!/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.