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Slurm Usage

Intro

SLURM (Simple Linux Utility for Resource Management) is a widely used open-source job scheduler that we use on GRIT HPC systems to allocate resources efficiently. This guide providies some basic information on how to use Slurm and create scripts for submitting jobs to the Slurm queue.

Typical Workflow

  1. Develop your program (e.g. on your computer and a subset of data)
  2. Update your program for use on HPC (e.g. change data paths if needed, etc.)
  3. Create a slurm job file (see below)
  4. Submit your job to the queue
  5. Monitor the job status, wait for completion

Steps 3-5 are detailed below.

Example Slurm job files

Slurm job files are writting in bash, which is a linux shell scripting language. Here's an example which uses one cpu on one computer to run a simple job, outputting any errors or other outputs to log files in the same directory. Note that on most GRIT HPC systems the main queue (aka partition in Slurm) is called 'basic'.

#!/bin/bash
## SLURM REQUIRED SETTINGS  <--- two hashtags are a comment in Slurm
#SBATCH --partition=basic   
#SBATCH --nodes=1
#SBATCH --ntasks=1
#SBATCH --cpus-per-task=1

## SLURM reads %x as the job name and %j as the job ID
#SBATCH --output=%x-%j.out
#SBATCH --error=%x-%j.err

# Job to run
./my_example_code.bash

Another Example:

#!/bin/bash
# 
#SBATCH -p basic # partition name (aka queue)
#SBATCH -c 1 # number of cores
#SBATCH --mem 100 # memory pool for all cores
#SBATCH -t 0-2:00 # time (D-HH:MM)
#SBATCH -o slurm.%N.%j.out # STDOUT
#SBATCH -e slurm.%N.%j.err # STDERR

# code or script to run 
for i in {1..100000}; do
echo $RANDOM >> SomeRandomNumbers.txt
donesort SomeRandomNumbers.txt

Python Example with Conda

The output goes to a file in your home directory called hello-python-*.out, which should contain a message from python.

#!/bin/bash

## SLURM REQUIRED SETTINGS1G
#SBATCH --nodes=1
#SBATCH --ntasks=1
#SBATCH --cpus-per-task=1 

## SLURM reads %x as the job name and %j as the job ID
#SBATCH --output=%x-%j.out
#SBATCH --error=%x-%j.err

#SBATCH --job-name=hello-python     # create a short name for your job
#SBATCH --time=00:01:00          # total run time limit (HH:MM:SS)

## Example use of Conda:

# first source bashrc (with conda.sh), then conda can be used
source ~/.bashrc

# make sure conda base is activated
conda activate

# Other conda commands go here


## run python 
python hello.py

hello.py should be something like this:

print('Hello from python!')

Adding details to Slurm job files

These examples are all very simple, so here are some useful commands for adding more complexity, such as more memory, more CPU's etc. Adding these requires finding out facts about the computer for the job file:

Find the number of CPU cores on a computer from the command line:

[user@computer ~]$ grep 'cpu cores' /proc/cpuinfo | uniq
cpu cores	: 48                   <---- an example output

Find out how much memory a computer has:

[user@computer ~]$ free -h
              total        used        free      shared  buff/cache   available
Mem:          1.5Ti       780Gi       721Gi       1.5Gi       8.6Gi       721Gi
Swap:          31Gi          0B        31Gi

For most of our use cases, one node and one task is all that is needed. More than this requires special code such as mpi4py (MPI = Message Passing Interface), or the Parallel computing toolbox such as with MATLAB (which uses --cpus-per-task). To request N cores for a job, just replace N with the number of cores you need in the Slurm job file, such as:

#SBATCH --nodes=1
#SBATCH --ntasks=1
#SBATCH --cpus-per-task=N

To get the max value for N for a computer:

[user@computer ~]$ scontrol show node | grep CPU
CPUAlloc=20 CPUTot=95 CPULoad=1.00

'CPUTot' is the max value for N.

Find the queue names:

[user@computer ~]$ sinfo
PARTITION AVAIL  TIMELIMIT  NODES  STATE NODELIST
basic*       up   infinite      1   idle   <--- in this case the queue name is 'basic' and it's the default
                                                 as indicated by the *

Submitting your job to the queue

Assuming you have a Slurm job file named slurm_test.sh:

#  test a job submission (don't run)
[user@computer ~]$ sbatch --test-only slurm_test.sh

#  run a job
[user@computer ~]$ sbatch slurm_test.sh

Monitoring your job

Examples of how this is done:

[user@computer ~]$ squeue
             JOBID PARTITION     NAME     USER ST       TIME  NODES NODELIST(REASON)
            166626 basic my_code  username PD       0:00     1 (Resources)
            166627 basic my_code  username  R       3:04     1 anvil

In the above, 'R' denotes that the job is running, 'PD' denotes that Slurm is waiting for resources.

You can also monitor the output by watching the log files from the command line. This will show the last few lines of the log file and update as the log file changes:

[user@computer ~]$ tail -f log-file-name.txt

Cancel the job if needed:

[user@computer ~]$ scancel 22           # cancel job 22

You can get the job number from squeue (e.g. JOBID).

Useful Commands

sinfo                # general info about slurm
sinfo -lNe           # more detailed info reporting with long format and nodes listed individually
scontrol show job 2  # show control info on job 2

To find the number of cores per socket:

lscpu | grep "Core(s) per socket" | awk '{print $4}'

More Example Job scripts

An example with R

#!/bin/bash -l
# How long should I job run for
#SBATCH --time=01:00:00
# Number of CPU cores, in this case 1 core
#SBATCH --ntasks=1
# Number of compute nodes to use
#SBATCH --nodes=1
# Name of the output files to be created. If not specified the outputs will be joined
#SBATCH --output=%x.%j.out
#SBATCH --error=%x.%j.err

# The code you want to run in your job
Rscript test_forge_r.R

Here's what was used in the test script test_forge_r.R:

# A simple R script to print hello world!
aString = "Hello World!"

print (aString)

References

https://www.carc.usc.edu/user-information/user-guides/hpc-basics/slurm-templates

https://docs.rc.fas.harvard.edu/kb/convenient-slurm-commands/

https://csc.cnsi.ucsb.edu/docs/slurm-job-scheduler

Genomics related: https://wiki.itap.purdue.edu/display/CGSB/How-to+Genomics

Python: https://rcpedia.stanford.edu/topicGuides/jobArrayPythonExample.html

https://login.scg.stanford.edu/faqs/cores/

https://stackoverflow.com/questions/65603381/slurm-nodes-tasks-cores-and-cpus

Regarding nodes vs tasks vs cpus vs cores: Here's a very good writeup: https://researchcomputing.princeton.edu/support/knowledge-base/scaling-analysis.

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