I don't know how many times I've read the docs; I keep thinking I understand it, but something is really wrong with prioritisation on our cluster, and we're struggling to understand why.

The setup:

1. We have a group who submit two types of work; production jobs and research jobs. 2. We have two sacctmgr accounts for this; let's call those 'prod' and 'research'. 3. We also have some dedicated hardware that they paid for which can be used only by users associated with the prod account.

Desired behaviour:

1. Usage of their dedicated hardware by production jobs should not hugely decrease the fairshare priority for research jobs in other partitions. 2. Usage of shared hardware should decrease their fairshare priority (whether by production or research jobs) 3. Memory should make a relatively small contribution to TRES usage (it's not normally the constrained resource)

Our approach:

Set TRESBillingWeights for cpu, memory and gres/GPU usage on shared partitions. Typically these are set to: CPU=1.0,Mem=0.25G,GRES/gpu=1.0    Set TRESBillingWeights to something small on the dedicated hardware partition, such as: CPU=0.25    Set PriorityWeightFairshare and PriorityWeightAge to values such that Fairshare dominates when jobs are young, and Age takes over if they've been pending a long time

The observed behaviour:    1. production association jobs have a high priority; this is working well    2. research jobs are still getting heavily penalised in fairshare, and we don't understand why; they seem to have enormous RawUsage, largely coming from memory:

Here's what I see from sshare (sensitive details removed, obviously):

sshare -l -A prod, research -a -o Account,RawUsage,EffectvUsage,FairShare,LevelFS,TRESRunMins%80 | grep -v cpu=0

'

Account RawUsage EffectvUsage FairShare LevelFS TRESRunMins

-------------------- ----------- ------------- ---------- ---------- --------------------------------------------------------------------------------

prod 1587283 0.884373 0.226149 cpu=81371,mem=669457237,energy=0,node=20610,billing=100833,fs/disk=0,vmem=0,pag+

prod 1082008 0.681681 0.963786 0.366740 cpu=81281,mem=669273429,energy=0,node=20520,billing=100833,fs/disk=0,vmem=0,pag+

prod 505090 0.318202 0.964027 0.785664 cpu=90,mem=184320,energy=0,node=90,billing=0,fs/disk=0,vmem=0,pages=0,gres/gpu=+

research 1043560787 0.380577 0.121648 cpu=17181098808,mem=35196566339054,energy=0,node=4295361360,billing=25773481938+

research 146841 0.000141 0.005311 124.679238 cpu=824,mem=3375923,energy=0,node=824,billing=824,fs/disk=0,vmem=0,pages=0,gres+

research 17530141 0.016798 0.001449 1.044377 cpu=254484,mem=3379938816,energy=0,node=161907,billing=893592,fs/disk=0,vmem=0,+

research 167597 0.000161 0.005070 109.238498 cpu=7275,mem=223516160,energy=0,node=7275,billing=50931,fs/disk=0,vmem=0,pages=+

research 12712481 0.012182 0.001931 1.440166 cpu=186327,mem=95399526,energy=0,node=23290,billing=232909,fs/disk=0,vmem=0,pag+

research 11521011 0.011040 0.002173 1.589104 cpu=8167,mem=267626086,energy=0,node=8167,billing=65338,fs/disk=0,vmem=0,pages=+

research 9719735 0.009314 0.002414 1.883599 cpu=15020,mem=69214617,energy=0,node=1877,billing=3755,fs/disk=0,vmem=0,pages=0+

research 25004766 0.023961 0.001207 0.732184 cpu=590778,mem=6464600473,energy=0,node=98910,billing=2266887,fs/disk=0,vmem=0,+

research 68938740 0.066061 0.000724 0.265570 cpu=159332,mem=963064985,energy=0,node=89957,billing=192706,fs/disk=0,vmem=0,pa+

research 7359413 0.007052 0.002656 2.487710 cpu=81401,mem=583487624,energy=0,node=20350,billing=20350,fs/disk=0,vmem=0,page+

research 718714430 0.688714 0.000241 0.025473 cpu=20616,mem=337774728,energy=0,node=5154,billing=92772,fs/disk=0,vmem=0,pages+

research 1016606 0.000974 0.003863 18.009010 cpu=17179774580,mem=35184178340113,energy=0,node=4294943645,billing=25769661870+

Firstly, why are the mem TRES numbers so enormous? Secondly, what's going on with the last user, where the rawusage is tiny, but the TRESRunMins is ridiculously big? That could be messing up the whole thing.

Thanks in advance for any advice (either that can help explain what I've misunderstood, or to suggestions of "there's a better way to achieve what you want")

Tim

--

Tim Cutts

Scientific Computing Platform Lead

AstraZeneca

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