[Bioc-devel] BiocParallel

I have been booked up so no chance to deploy but I do have access to SGE and
LSF so will try and will report ASAP.

On Tue, Dec 4, 2012 at 4:08 AM, Hahne, Florian <florian.hahne at novartis.com>
wrote:

Hi Henrik,
I have now come up now with a relatively generic version of this
SGEcluster approach. It does indeed use BatchJobs under the hood and
should thus support all available cluster queues, assuming that the
necessary batchJobs routines are available. I could only test this on our
SGE cluster, but Vince wanted to try other queuing systems. Not sure how
far he got. For now the code is wrapped in a little package called
Qcluster with some documentation. If you want to I can send you a version
in a separate mail. Would be good to test this on other systems, and I am
sure there remain some bugs that need to be ironed out. In particular the
fault tolerance you mentioned needs to be addressed properly. Currently
the code may leave unwanted garbage if things fail in the wrong places
because all the communication is file-based.
Martin, I'll send you my updated version in case you want to include this
in biocParallel for others to contribute.
Florian
--






On 12/4/12 5:46 AM, "Henrik Bengtsson" <hb at biostat.ucsf.edu> wrote:

Picking up this thread in lack of other places (= were should
BiocParallel be discussed?)

I saw Martin's updates on the BiocParallel - great.  Florian's SGE
scheduler was also mentioned; is that one built on top of BatchJobs?
If so I'd be interested in looking into that/generalizing that to work
with any BatchJobs scheduler.

I believe there is going to be a new release of BatchJobs rather soon,
so it's probably worth waiting until that is available.

The main use case I'm interested in is to launch batch jobs on a
PBS/Torque cluster, and then use multicore processing on each compute
node.  It would be nice to be able to do this using the BiocParallel
model, but maybe it is too optimistic to get everything to work under
same model.  Also, as Vince hinted, fault tolerance etc needs to be
addressed and needs to be addressed differently in the different
setups.

/Henrik

On Tue, Nov 20, 2012 at 6:59 AM, Ramon Diaz-Uriarte <rdiaz02 at gmail.com>
wrote:

On Sat, 17 Nov 2012 13:05:29 -0800,"Ryan C. Thompson"
<rct at thompsonclan.org> wrote:

On 11/17/2012 02:39 AM, Ramon Diaz-Uriarte wrote:

In addition to Steve's comment, is it really a good thing that "all

code

stays the same."?  I mean, multiple machines vs. multiple cores are,
often, _very_ different things: for instance, shared vs. distributed
memory, communication overhead differences, whether or not you can

assume

packages and objects to be automagically present in the slaves/child
process, etc. So, given they are different situations, I think it
sometimes makes sense to want to write different code for each

situation

(I often do); not to mention Steve's hybrid cases ;-).


Since BiocParallel seems to be a major undertaking, maybe it would
be
appropriate to provide a flexible approach, instead of hard wiring

the

foreach approach.

Of course there are cases where the same code simply can't work for
both
multicore and multi-machine situations, but those generally don't fall
into the category of things that can be done using lapply. Lapply and
all of its parallelized buddies like mclapply, parLapply, and foreach
are designed for data-parallel operations with no interdependence
between results, and these kinds of operations generally parallelize
as
well across machines as across cores, unless your network is not fast
enough (in which case you would choose not to use multi-machine
parallelism). If you want a parallel algorithm for something like the
disjoin method of GRanges, you might need to write some special
purpose
code, and that code might be very different for multicore vs
multi-machine.

So yes, sometimes there is a fundamental reason that you have to
change
the code to make it run on multiple machines, and neither foreach nor
any other parallelization framework will save you from having to
rewrite
your code. But often there is no fundamental reason that the code has
to
change, but you end up changing it anyway because of limitations in
your
parallelization framework. This is the case that foreach saves you
from.

Hummm... I guess you are right, and we are talking about "often" or
"most
of the time", which is where all this would fit. Point taken.


Best,

R.






--
Ramon Diaz-Uriarte
Department of Biochemistry, Lab B-25
Facultad de Medicina
Universidad Aut?noma de Madrid
Arzobispo Morcillo, 4
28029 Madrid
Spain

Phone: +34-91-497-2412

Email: rdiaz02 at gmail.com
       ramon.diaz at iib.uam.es

http://ligarto.org/rdiaz