[slurm-users] Exposing only requested CPUs to a job on a given node.

Renfro, Michael Renfro at tntech.edu
Fri May 14 22:40:46 UTC 2021

Untested, but prior experience with cgroups indicates that if things are working correctly, even if your code tries to run as many processes as you have cores, those processes will be confined to the cores you reserve.

Try a more compute-intensive worker function that will take some seconds or minutes to complete, and watch the reserved node with 'top' or a similar program. If for example, the job reserved only 1 core and tried to run 20 processes, you'd see 20 processes in 'top', each at 5% CPU time.

To make the code a bit more polite, you can import the os module and create a new variable from the SLURM_CPUS_ON_NODE environment variable to guide Python into starting the correct number of processes:

                cpus_reserved = int(os.environ['SLURM_CPUS_ON_NODE'])

From: slurm-users <slurm-users-bounces at lists.schedmd.com> on behalf of Rodrigo Santibáñez <rsantibanez.uchile at gmail.com>
Date: Friday, May 14, 2021 at 5:17 PM
To: Slurm User Community List <slurm-users at lists.schedmd.com>
Subject: Re: [slurm-users] Exposing only requested CPUs to a job on a given node.

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Hi you all,

I'm replying to have notifications answering this question. I have a user whose python script used almost all CPUs, but configured to use only 6 cpus per task. I reviewed the code, and it doesn't have an explicit call to multiprocessing or similar. So the user is unaware of this behavior (and also me).

Running slurm 20.02.6


On Fri, May 14, 2021 at 1:37 PM Luis R. Torres <lrtorres at gmail.com<mailto:lrtorres at gmail.com>> wrote:
Hi Folks,

We are currently running on SLURM 20.11.6 with cgroups constraints for memory and CPU/Core.  Can the scheduler only expose the requested number of CPU/Core resources to a job?  We have some users that employ python scripts with the multi processing modules, and the scripts apparently use all of the CPU/Cores in a node, despite using options to constraint a task to just a given number of CPUs.    We would like several multiprocessing jobs to run simultaneously on the nodes, but not step on each other.

The sample script I use for testing is below; I'm looking for something similar to what can be done with the GPU Gres configuration where only the number of GPUs requested are exposed to the job requesting them.

#!/usr/bin/env python3

import multiprocessing

def worker():

    print("Worker on CPU #%s" % multiprocessing.current_process



    for j in range(20):

      result += j**2

    print ("Result on CPU {} is {}".format(multiprocessing.curr



if __name__ == '__main__':

    pool = multiprocessing.Pool()

    jobs = []

    print ("This host exposed {} CPUs".format(multiprocessing.c


    for i in range(multiprocessing.cpu_count()):

        p = multiprocessing.Process(target=worker, name=i).star


Luis R. Torres
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