[Bioc-devel] BiocParallel: BatchJobs backend (Was: Re: BiocParallel)

Hi Henrik,

Sorry for the late response. Suggestions and feedback are always
welcome. I just forgot to enable the issue tracker (now enabled).

For prototyping I usually use Interactive/Multicore, but I'll
regularly test on our local clusters which use Torque or Slurm,
respectively.

Michel

2013/6/7 Henrik Bengtsson <hb at biostat.ucsf.edu>:

Great - this looks promising already.

What's your test system(s), beyond standard SSH and multicore
clusters?  I'm on a Torque/PBS system.

I'm happy to test, give feedback etc.  I don't see an 'Issues' tab on
the GitHub page.  Michel, how do you prefer to get feedback?

/Henrik


On Thu, Jun 6, 2013 at 5:21 PM, Michael Lawrence
<lawrence.michael at gene.com> wrote:

And here is the on-going development of the backend:
https://github.com/mllg/BiocParallel/tree/batchjobs

Not sure how well it's been tested.

Kudos to Michel Lang for making so much progress so quickly.

Michael

On Thu, Jun 6, 2013 at 1:59 PM, Dan Tenenbaum <dtenenba at fhcrc.org>
wrote:

On Thu, Jun 6, 2013 at 1:56 PM, Henrik Bengtsson <hb at biostat.ucsf.edu>
wrote:

Hi, I'd like to pick up the discussion on a BatchJobs backend for
BiocParallel where it was left back in Dec 2012 (Bioc-devel thread
'BiocParallel'

[https://stat.ethz.ch/pipermail/bioc-devel/2012-December/003918.html]).

Florian, would you mind sharing your BatchJobs backend code?  Is it
independent of BiocParallel and/or have you tried it with the most
recent BiocParallel implementation
[https://github.com/Bioconductor/BiocParallel/]?

You should be aware that there is  Google Summer of Code project in
progress to address this.

http://www.bioconductor.org/developers/gsoc2013/ (towards the bottom)

Dan

/Henrik

On Tue, Dec 4, 2012 at 12:38 PM, Henrik Bengtsson

<hb at biostat.ucsf.edu>

wrote:

Thanks.

On Tue, Dec 4, 2012 at 3:47 AM, Vincent Carey
<stvjc at channing.harvard.edu> wrote:

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.

...and I'll try it out on PBS (... but I most likely won't have

time

to do this until the end of the year).

Henrik

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