Indeed, and you can quite easily get into a “boulders and sand” scheduling
problem; if you allow the small interactive jobs (the sand) free access to
everything, the scheduler tends to find them easy to schedule, partially fills
nodes with them, and then finds it can’t find contiguous resources large enough
for the big parallel jobs (the boulders), and you end up with the large batch
jobs pending forever.
I’ve tried various approaches to this in the past; for example pre-emption of
large long running jobs, but that causes resource starvation (suspended jobs
are still consuming virtual memory) and then all sorts of issues with timeouts
on TCP connections and so on and so forth, these being genomics jobs with lots
of not-normal-HPC activities like talking to relational databases etc.
I think you always end up having to ring-fence hardware for the large parallel
batch jobs, and not allow the interactive stuff on it.
This of course is what leads some users to favour the cloud, because it appears
to be infinite, and so the problem appears to go away. But let's not get into
that argument here.
Tim
On 16 Jan 2020, at 23:50, Alex Chekholko via Beowulf
<beowulf@beowulf.org<mailto:beowulf@beowulf.org>> wrote:
Hey Jim,
There is an inverse relationship between latency and throughput. Most
supercomputing centers aim to keep their overall utilization high, so the queue
always needs to be full of jobs.
If you can have 1000 nodes always idle and available, then your 1000 node jobs
will usually take 10 seconds. But your overall utilization will be in the low
single digit percent or worse.
Regards,
Alex
On Thu, Jan 16, 2020 at 3:25 PM Lux, Jim (US 337K) via Beowulf
<beowulf@beowulf.org<mailto:beowulf@beowulf.org>> wrote:
Are there any references out there that discuss the tradeoffs between
interactive and batch scheduling (perhaps some from the 60s and 70s?) –
Most big HPC systems have a mix of giant jobs and smaller ones managed by some
process like PBS or SLURM, with queues of various sized jobs.
What I’m interested in is the idea of jobs that, if spread across many nodes
(dozens) can complete in seconds (<1 minute) providing essentially
“interactive” access, in the context of large jobs taking days to complete.
It’s not clear to me that the current schedulers can actually do this – rather,
they allocate M of N nodes to a particular job pulled out of a series of
queues, and that job “owns” the nodes until it completes. Smaller jobs get run
on (M-1) of the N nodes, and presumably complete faster, so it works down
through the queue quicker, but ultimately, if you have a job that would take,
say, 10 seconds on 1000 nodes, it’s going to take 20 minutes on 10 nodes.
Jim
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