[slurm-users] Avoiding fragmentation

[Gerhard Strangar via slurm-users]

Hi,

I'm trying to figure out how to deal with a mix of few- and many-cpu
jobs. By that I mean most jobs use 128 cpus, but sometimes there are
jobs with only 16. As soon as that job with only 16 is running, the
scheduler splits the next 128 cpu jobs into 96+16 each, instead of
assigning a full 128 cpu node to them. Is there a way for the
administrator to achieve preferring full nodes?
The existence of pack_serial_at_end makes me believe there is not,
because that basically is what I needed, apart from my serial jobs using
16 cpus instead of 1.

Gerhard

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[Loris Bennett via slurm-users]

Hi Gerhard,

Gerhard Strangar via slurm-users <slurm...@lists.schedmd.com> writes:

> Hi,
>
> I'm trying to figure out how to deal with a mix of few- and many-cpu
> jobs. By that I mean most jobs use 128 cpus, but sometimes there are
> jobs with only 16. As soon as that job with only 16 is running, the
> scheduler splits the next 128 cpu jobs into 96+16 each, instead of
> assigning a full 128 cpu node to them. Is there a way for the
> administrator to achieve preferring full nodes?
> The existence of pack_serial_at_end makes me believe there is not,
> because that basically is what I needed, apart from my serial jobs using
> 16 cpus instead of 1.
>
> Gerhard

This may well not be relevant for your case, but we actively discourage
the use of full nodes for the following reasons:

- When the cluster is full, which is most of the time, MPI jobs in
general will start much faster if they don't specify the number of
nodes and certainly don't request full nodes. The overhead due to
the jobs being scattered across nodes is often much lower than the
additional waiting time incurred by requesting whole nodes.

- When all the cores of a node are requested, all the memory of the
node becomes unavailable to other jobs, regardless of how much
memory is requested or indeed how much is actually used. This holds
up jobs with low CPU but high memory requirements and thus reduces
the total throughput of the system.

These factors are important for us because we have a large number of
single core jobs and almost all the users, whether doing MPI or not,
significantly overestimate the memory requirements of their jobs.

Cheers,

Loris

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

[Cutts, Tim via slurm-users]

Agree with that.   Plus, of course, even if the jobs run a bit slower by not having all the cores on a single node, they will be scheduled sooner, so the overall turnaround time for the user will be better, and ultimately that's what they care about.  I've always been of the view, for any scheduler, that the less you try to constrain it the better.  It really depends on what you're trying to optimise for, but generally speaking I try to optimise for maximum utilisation and throughput, unless I have a specific business case that needs to prioritise particular workloads, and then I'll compromise on throughput to get the urgent workload through sooner.

Tun

---

From: Loris Bennett via slurm-users <slurm...@lists.schedmd.com>
Sent: 09 April 2024 06:51
To: slurm...@lists.schedmd.com <slurm...@lists.schedmd.com>
Cc: Gerhard Strangar <g...@arcor.de>
Subject: [slurm-users] Re: Avoiding fragmentation

 

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[Paul Edmon via slurm-users]

I wrote a little blog post on this topic a few years back: https://www.rc.fas.harvard.edu/blog/cluster-fragmentation/

It's a vexing problem, but as noted by the other responders it is something that depends on your cluster policy and job performance needs. Well written MPI code should be able to scale well even when given non-optimal topologies.

You might also look at Node Weights (https://slurm.schedmd.com/slurm.conf.html#OPT_Weight). We use them on mosaic partitions so that the latest hardware is left available for larger jobs needing more performance.  You can also use it to force jobs to one side of the partition, though generally the scheduler does this automatically.

-Paul Edmon-

[Juergen Salk via slurm-users]

Hi Gerhard,

I am not sure if this counts as administrative measure, but we do
highly encourage our users to always explicitely specify --nodes=n
together with --ntasks-per-node=m (rather than just --ntasks=n*m and
omitting --nodes option, which may lead to cores allocated here and
there and everywhere as long as network topology allows this).

I do understand Loris' and Tim's arguments, but for certain reasons we
have configured single user node access policy (ExclusiveUser=YES),
which allows multiple jobs to share a node, but only jobs owned by
one and the same user. So we also try to avoid fragmentation whenever
possible and want users to pack their jobs as densely as possible on
the nodes in order to leave as many nodes as possible available for
others. For us, this works reasonably well in terms of core
utilization because we have almost no users who submit only one or two
few-core jobs at a time but usually whole bunches of such jobs
(sometimes hundreds) at once of which multiple jobs then
simultaneously run on the individual nodes. That keeps the waste of
unallocated cores on individual nodes within acceptable limits for us.

Best regards
Jürgen


* Loris Bennett via slurm-users <slurm...@lists.schedmd.com> [240409 07:51]:

--
Jürgen Salk
Scientific Software & Compute Services (SSCS)
Kommunikations- und Informationszentrum (kiz)
Universität Ulm
Telefon: +49 (0)731 50-22478
Telefax: +49 (0)731 50-22471

[Williams, Jenny Avis via slurm-users]

Various options that might help reduce job fragmentation.

Turn up debugging on slurmctld and add the DebugFlags like TraceJobs, SelectType, and Steps. With debugging set high enough one can see a good bit of the logic in regard to node selection.

CR_LLN Schedule resources to jobs on the least loaded nodes
(based upon the number of idle CPUs). This is generally
only recommended for an environment with serial jobs as
idle resources will tend to be highly fragmented, result-
ing in parallel jobs being distributed across many nodes.
Note that node Weight takes precedence over how many idle
resources are on each node. Also see the partition con-
figuration parameter LLN use the least loaded nodes in
selected partitions.

Explore node weights. If your nodes are not identical apply node weights to sort your nodes in the order of how you wish them to be selected; on the other hand, even for homogenous nodes you might try sets of weights to have the scheduler within a given scheduling cycle consider a smaller number of nodes of a weight before then considering the next number of nodes of the next weight. The number of nodes within a weight set might be no smaller than 1/3 or 1/4 of the total partition size. YMMV based on for instance ratio of serial jobs to MPI jobs, job length, etc. I have seen evidence that node allocation progresses roughly this way.

Turn on backfill and educate users to better fit both their job resource requirements and the job runtime. This will allow backfill to work more efficiently. Note that backfill choices are made within a given set of job within a partition.


CR_Pack_Nodes
If a job allocation contains more resources than will be
used for launching tasks (e.g. if whole nodes are allo-
cated to a job), then rather than distributing a job's
tasks evenly across its allocated nodes, pack them as
tightly as possible on these nodes. For example, consider
a job allocation containing two entire nodes with eight
CPUs each. If the job starts ten tasks across those two
nodes without this option, it will start five tasks on
each of the two nodes. With this option, eight tasks will
be started on the first node and two tasks on the second
node. This can be superseded by "NoPack" in srun's
"--distribution" option. CR_Pack_Nodes only applies when
the "block" task distribution method is used.

pack_serial_at_end
If used with the select/cons_res or select/cons_tres plug-
in, then put serial jobs at the end of the available nodes
rather than using a best fit algorithm. This may reduce
resource fragmentation for some workloads.

reduce_completing_frag
This option is used to control how scheduling of resources
is performed when jobs are in the COMPLETING state, which
influences potential fragmentation. If this option is not
set then no jobs will be started in any partition when any
job is in the COMPLETING state for less than CompleteWait
seconds. If this option is set then no jobs will be
started in any individual partition that has a job in COM-
PLETING state for less than CompleteWait seconds. In
addition, no jobs will be started in any partition with
nodes that overlap with any nodes in the partition of the
completing job. This option is to be used in conjunction
with CompleteWait.