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From Daniel Siegmann <daniel.siegm...@velos.io>
Subject Re: heterogeneous cluster hardware
Date Thu, 21 Aug 2014 20:34:32 GMT
If you use Spark standalone, you could start multiple workers on some
machines. Size your worker configuration to be appropriate for the weak
machines, and start multiple on your beefier machines.

It may take a bit of work to get that all hooked up - probably you'll want
to write some scripts to start everything on all your nodes correctly. But
hopefully it will work smoothly once the cluster is up and running.


On Thu, Aug 21, 2014 at 11:42 AM, anthonyjschulte@gmail.com <
anthonyjschulte@gmail.com> wrote:

> Jörn, thanks for the post...
>
> Unfortunately, I am stuck with the hardware I have and might not be
> able to get budget allocated for a new stack of servers when I've
> already got so many "ok" servers on hand... And even more
> unfortunately, a large subset of these machines are... shall we say...
> extremely humble in their cpus and ram. My group has exclusive access
> to the machine and rarely do we need to run concurrent jobs-- What I
> really want is max capacity per-job. The applications are massive
> machine-learning experiments, so I'm not sure about the feasibility of
> breaking it up into concurrent jobs. At this point, I am seriously
> considering dropping down to Akka-level programming. Why, oh why,
> doesn't spark allow for allocating variable worker threads per host?
> this would seem to be the correct point of abstraction that would
> allow the construction of massive clusters using "on-hand" hardware?
> (the scheduler probably wouldn't have to change at all)
>
> On Thu, Aug 21, 2014 at 9:25 AM, Jörn Franke [via Apache Spark User
> List] <[hidden email] <http://user/SendEmail.jtp?type=node&node=12587&i=0>>
> wrote:
>
> > Hi,
> >
> > Well, you could use Mesos or Yarn2 to define  resources per Job - you
> can
> > give only as much resources (cores, memory etc.) per machine as your
> "worst"
> > machine has. The rest is done by Mesos or Yarn. By doing this you avoid
> a
> > per machine resource assignment without any disadvantages. You can run
> > without any problems run other jobs in parallel and older machines won't
> get
> > overloaded.
> >
> > however, you should take care that your cluster does not get too
> > heterogeneous.
> >
> > Best regards,
> > Jörn
> >
> > Le 21 août 2014 16:55, "[hidden email]" <[hidden email]> a écrit :
> >>
> >> I've got a stack of Dell Commodity servers-- Ram~>(8 to 32Gb) single or
> >> dual
> >> quad core processor cores per machine. I think I will have them loaded
> >> with
> >> CentOS. Eventually, I may want to add GPUs on the nodes to handle
> linear
> >> alg. operations...
> >>
> >> My Idea has been:
> >>
> >> 1) to find a way to configure Spark to allocate different resources
> >> per-machine, per-job. -- at least have a "standard executor"... and
> allow
> >> different machines to have different numbers of executors.
> >>
> >> 2) make (using vanilla spark) a pre-run optimization phase which
> >> benchmarks
> >> the throughput of each node (per hardware), and repartition the dataset
> to
> >> more efficiently use the hardware rather than rely on Spark
> Speculation--
> >> which has always seemed a dis-optimal way to balance the load across
> >> several
> >> differing machines.
> >>
> >>
> >>
> >>
> >> --
> >> View this message in context:
> >>
> http://apache-spark-user-list.1001560.n3.nabble.com/heterogeneous-cluster-hardware-tp11567p12581.html
> >> Sent from the Apache Spark User List mailing list archive at
> Nabble.com.
> >>
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> >
> >
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>
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> View this message in context: Re: heterogeneous cluster hardware
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-- 
Daniel Siegmann, Software Developer
Velos
Accelerating Machine Learning

440 NINTH AVENUE, 11TH FLOOR, NEW YORK, NY 10001
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