[Neuroimaging] parallel computation of bundle_distances_mam/mdf ?

Samuel St-Jean stjeansam at gmail.com
Wed Dec 14 11:14:17 EST 2016 

That also depends on which version of clang ships by default with osx, in
which case you have to play around with it to get a new one. I think it
starts with clang 3.7 to have openmp support (I only ever tried Mac osx
10.9, so anyone more experienced can chime in), but everything older has to
go through the hombebrew gcc install and company. Might be worhtwhile to
check if openmp support is out of the box now, and starting on which mac
osx versions, since older ones could be problematic for first time
installs.

I also have to admit I have no idea how old is old in the mac world, so
maybe 10.9 is already phased out by now, but it was a hard and time
consuming building around stuff with homebrew experience (and again, first
time I used a mac, so, I guess the average user would also have some
issues).

Samuel

2016-12-14 16:51 GMT+01:00 Eleftherios Garyfallidis <elef at indiana.edu>:

>
> Hi Emanuele,
>
> My understanding is that openmp was only temporarily not available when
> clang replaced gcc in osx.
>
> So, I would suggest to go ahead with openmp. Any current installation
> issues are only temporarily for osx.
> Openmp gives us a lot of capability to play with shared memory and it is a
> standard that will be around
> for very long time. Also, the great integration in cython makes the
> algorithms really easy to read.
> So, especially for this project my recommendation is to use openmp rather
> than multiprocessing. All the way! :)
>
> I am CC'ing Stephan who wrote the instructions for osx. I am sure he can
> help you with this. I would also suggest
> to check if xcode provides any new guis for enabling openmp. I remember
> there was something for that.
>
> Laterz!
> Eleftherios
>
>
>
>
> On Wed, Dec 14, 2016 at 6:29 AM Emanuele Olivetti <olivetti at fbk.eu> wrote:
>
>> Hi Eleftherios,
>>
>> Thank you for pointing me to the MDF example. From what I see the Cython
>> syntax is not complex, which is good.
>>
>> My only concern is the availability of OpenMP in the systems where DiPy
>> is used. On a reasonably recent GNU/Linux machine it seems straightforward
>> to have libgomp and the proper version of gcc. On other systems - say OSX -
>> the situation is less clear to me. According to what I read here
>>   http://nipy.org/dipy/installation.html#openmp-with-osx
>> the OSX installation steps are not meant for standard end users. Are
>> those instructions updated?
>> As a test of that, we've just tried to skip the steps described above and
>> instead to install gcc with conda on OSX ("conda install gcc"). In the
>> process, conda installed the recent gcc-4.8 with libgomp, which seems good
>> news. Unfortunately, when we tried to compile a simple example of Cython
>> code using parallelization (see below), the process failed (fatal error:
>> limits.h : no such file or directory)...
>>
>> For the reasons above, I am wondering whether the very simple solution of
>> using the "multiprocessing" module, available from the standard Python
>> library, may be an acceptable first step towards the more efficient
>> multithreading of Cython/libgomp. With "multiprocessing", there is no extra
>> dependency on libgomp, or recent gcc or else. Moreover, multiprocessing
>> does not require to have Cython code, because it works on plain Python too.
>>
>> Best,
>>
>> Emanuele
>>
>> ---- test.pyx ----
>> from cython import parallel
>> from libc.stdio cimport printf
>>
>> def test_func():
>>     cdef int thread_id = -1
>>     with nogil, parallel.parallel(num_threads=10):
>>         thread_id = parallel.threadid()
>>         printf("Thread ID: %d\n", thread_id)
>> -----
>>
>> ----- setup.py -----
>> from distutils.core import setup, Extension
>> from Cython.Build import cythonize
>>
>> extensions = [Extension(
>>                 "test",
>>                 sources=["test.pyx"],
>>                 extra_compile_args=["-fopenmp"],
>>                 extra_link_args=["-fopenmp"]
>>             )]
>>
>> setup(
>>     ext_modules = cythonize(extensions)
>> )
>> ----
>> python setup.py build_ext --inplace
>>
>> On Tue, Dec 13, 2016 at 11:17 PM, Eleftherios Garyfallidis <
>> elef at indiana.edu> wrote:
>>
>> Hi Emanuele,
>>
>> Here is an example of how we calculated the distance matrix in parallel
>> (for the MDF) using OpenMP
>> https://github.com/nipy/dipy/blob/master/dipy/align/bundlemin.pyx
>>
>> You can just add another function that does the same using mam. It should
>> be really easy to implement as we have
>> already done it for the MDF for speeding up SLR.
>>
>> Then we need to update the bundle_distances* functions to use the
>> parallel versions.
>>
>> I'll be happy to help you with this. Let's try to schedule some time to
>> look at this together.
>>
>> Best regards,
>> Eleftherios
>>
>>
>> On Mon, Dec 12, 2016 at 11:16 AM Emanuele Olivetti <olivetti at fbk.eu>
>> wrote:
>>
>> Hi,
>>
>> I usually compute the distance matrix between two lists of streamlines
>> using bundle_distances_mam() or bundle_distances_mdf(). When the lists are
>> large, it is convenient and easy to exploit the multiple cores of the CPU
>> because such computation is intrinsically (embarassingly) parallel. At the
>> moment I'm doing it through the multiprocessing or the joblib modules,
>> because I cannot find a way directly from DiPy, at least according to what
>> I see in dipy/tracking/distances.pyx . But consider that I am not
>> proficient in cython.parallel.
>>
>> Is there a preferable way to perform such parallel computation? I plan to
>> prepare a pull request in future and I'd like to be on the right track.
>>
>> Best,
>>
>> Emanuele
>>
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