I'd be interested
Prentice
On 9/23/21 10:37 AM, Pizarro, Angel via Beowulf wrote:
DISCLOSURE: I work for AWS HPC Developer Relations in the services
team. We developer AWS Batch, AWS ParallelCluster, NICE DCV, etc.
Lambda’s limits today are 128MB to 10,240MB (~10GB) and billed in 1MB
per ms increments. 15 minute max runtime for the function invocation.
Would you all be interested in a hands-on self-paced workshop on
creating (or porting) an application to serverless environment? E.g.
Monte-Carlo simulation, a genome alignment or variant call, or some
other problem? We have some basic data processing documentation but
nothing that speaks to real-world HPC use case and that is a something
I want to fill the gap on if folks are interested in it.
Dr. Denis Bauer at CSIRO is also doing interesting things with
serverless.
-angel
--
Angel Pizarro | Principal Developer Advocate, HPC @ AWS
*From: *Beowulf <beowulf-boun...@beowulf.org> on behalf of Guy Coates
<guy.coa...@gmail.com>
*Date: *Thursday, September 23, 2021 at 8:46 AM
*To: *Tim Cutts <t...@sanger.ac.uk>
*Cc: *Beowulf <beowulf@beowulf.org>
*Subject: *RE: [EXTERNAL] [Beowulf] Rant on why HPC isn't as easy as
I'd like it to be. [EXT]
*CAUTION*: This email originated from outside of the organization. Do
not click links or open attachments unless you can confirm the sender
and know the content is safe.
Out of interest, how large are the compute jobs (memory, runtime
etc)? How easy to get them to fit into a serverless environment?
Thanks,
Guy
On Tue, 21 Sept 2021 at 13:02, Tim Cutts <t...@sanger.ac.uk
<mailto:t...@sanger.ac.uk>> wrote:
I think that’s exactly the situation we’ve been in for a long
time, especially in life sciences, and it’s becoming more
entrenched. My experience is that the average user of our
scientific computing systems has been becoming less technically
savvy for many years now.
The presence of the cloud makes that more acute, in particular
because it makes it easy for the user to effectively throw more
hardware at the problem, which reduces the incentive to make their
code particularly fast or efficient. Cost is the only brake on
it, and in many cases I’m finding the PI doesn’t actually care
about that. They care that a result is being obtained (and it’s
time to first result they care about, not time to complete all the
analysis), and so they typically don’t have much time for those of
us who are telling them they need to invest in time up front
developing and optimising efficient code.
And cost is not necessarily the brake I thought it was going to be
anyway. One recent project we’ve done on AWS has impressed me a
great deal. It’s not terribly CPU efficient, and would doubtless,
with sufficient effort, run much more efficiently on premise. But
it’s extremely elastic in its nature, and so a good fit for the
cloud. Once a week, the project has to completely re-analyse the
600,000+ COVID genomes we’e sequenced so far, looking for new
branches in the phylogenetic tree, and to complete that analysis
inside 8 hours. Initial attempts to naively convert the HPC
implementation to run on AWS looked as though they were going to
be very expensive (~$50k per weekly run). But a fundamental
reworking of the entire workflow to make it as cloud native as
possible, by which I mean almost exclusively serverless, has
succeeded beyond what I expected. The total cost is <$5,000 a
month, and because there is essentially no statically configured
infrastructure at all, the security is fairly easy to be
comfortable about. And all of that was done with no detailed
thinking about whether the actual algorithms running in the
containers are at all optimised in a traditional HPC sense. It’s
just not needed for this particular piece of work. Did it need
software developers with hardcore knowledge of performance
optimisation? No. Was it rapid to develop and deploy? Yes. Is
the performance fast enough for UK national COVID variant
surveillance? Yes. Is it cost effective? Yes. Sold! The one
thing it did need was knowledgeable cloud architects, but the
cloud providers can and do help with that.
Tim
--
Tim Cutts
Head of Scientific Computing
Wellcome Sanger Institute
On 21 Sep 2021, at 12:24, John Hearns <hear...@gmail.com
<mailto:hear...@gmail.com>> wrote:
Some points well made here. I have seen in the past job
scripts passed on from graduate student to graduate student -
the case I am thinking on was an Abaqus script for 8 core
systems, being run on a new 32 core system. Why WOULD a
graduate student question a script given to them - which
works. They should be getting on with their science. I guess
this is where Research Software Engineers come in.
Another point I would make is about modern processor
architectures, for instance AMD Rome/Milan. You can have
different Numa Per Socket options, which affect performance.
We set the preferred IO path - which I have seen myself to
have an effect on latency of MPI messages. IF you are not
concerned about your hardware layout you would just go ahead
and run, missing a lot of performance.
I am now going to be controversial and common that over in
Julia land the pattern seems to be these days people develop
on their own laptops, or maybe local GPU systems. There is a
lot of microbenchmarking going on. But there seems to be not a
lot of thought given to CPU pinning or shat happens with
hyperthreading. I guess topics like that are part of HPC
'Black Magic' - though I would imagine the low latency crowd
are hot on them.
I often introduce people to the excellent lstopo/hwloc
utilities which show the layout of a system. Most people are
pleasantly surprised to find this.
-- The Wellcome Sanger Institute is operated by Genome Research
Limited, a charity registered in England with number 1021457 and a
company registered in England with number 2742969, whose
registered office is 215 Euston Road, London, NW1 2BE.
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Dr. Guy Coates
+44(0)7801 710224
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