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.ha...@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" <h...@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 <rdia...@gmail.com> > >wrote: > >> > >> > >> > >> On Sat, 17 Nov 2012 13:05:29 -0800,"Ryan C. Thompson" > >><r...@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 Autónoma de Madrid > >> Arzobispo Morcillo, 4 > >> 28029 Madrid > >> Spain > >> > >> Phone: +34-91-497-2412 > >> > >> Email: rdia...@gmail.com > >> ramon.d...@iib.uam.es > >> > >> http://ligarto.org/rdiaz > >> > >> _______________________________________________ > >> Bioc-devel@r-project.org mailing list > >> https://stat.ethz.ch/mailman/listinfo/bioc-devel > > [[alternative HTML version deleted]]
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