As you say, it does reduce overall throughput, because those large-gpu nodes are dedicated.  We don’t have many nodes in that partition, the rest are in our standard queues.  Ultimately, this is a tradeoff.  We have chosen to reduce total throughput slightly in order to make sure that large jobs actually get scheduled in a timely fashion.

 

This is the boulders-and-sand scheduling problem; I don’t think there’s any single perfect configuration, it just depends on what’s most important to your business; throughput, or the turnaround time of particular jobs.

 

Tim

 

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Tim Cutts

Senior Director, R&D IT - Data, Analytics & AI, Scientific Computing Platform

AstraZeneca

 

Find out more about R&D IT Data, Analytics & AI and how we can support you by visiting our Service Catalogue |

 

 

From: Loris Bennett via slurm-users <slurm-users@lists.schedmd.com>
Date: Friday, 4 April 2025 at 6:37 am
To: Slurm Users Mailing List <slurm-users@lists.schedmd.com>
Subject: [slurm-users] Re: Minimum cpu cores per node partition level configuration

Hi Tim,

"Cutts, Tim via slurm-users"

<slurm-users-rGrgPyRx505G7+FkpxDULAC/G2K4zDHf@public.gmane.org> writes:

> You can set a partition QoS which specifies a minimum.  We have such a qos on our large-gpu partition; we don’t want people scheduling small stuff to it, so we
> have this qos:

How does this affect total throughput?  Presumably, 'small' GPU jobs
might potentially have to wait for resources in other partitions, even
if resources are free in 'large-gpu'.  Do you other policies which
ameliorate this?

Cheers,

Loris

[snip (135 lines)]


--
Dr. Loris Bennett (Herr/Mr)
FUB-IT, Freie Universität Berlin

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