Hi all,
As Juan said, I didn't include the -lgomp flag for f2py. Once I use that,
the f2py module works with openMP as expected.
Thanks,
Brandt
>
> Message: 1
> Date: Thu, 21 Jul 2011 11:34:13 +0200
> From: Juan
> Subject: Re: [Numpy-discussion] f2py and openmp on mac os x with
>gfortran
Hello,
I'm struggling to create openmp subroutines. I've simplified the problem
down to the subroutine below.
-- play.f90 --
subroutine step(soln,n)
implicit none
integer n,i
real*8 soln(n)
!f2py intent(in) n
!f2py intent(out) soln
!f2py depend(n) soln
!$OMP PARALLEL DO
do i=1,n
Hi all,
Thanks for the replies. As mentioned, I'm parallelizing so that I can take
many inner products simultaneously (which I agree is embarrassingly
parallel). The library I'm writing asks the user to supply a function that
takes two objects and returns their inner product. After all the discussi
Hi all,
Thanks for your replies.
> Brandt Belson wrote:
> > Unfortunately I can't flatten the arrays. I'm writing a library where
> > the user supplies an inner product function for two generic objects, and
> > almost always the inner product function does large ar
Content-Type: text/plain; charset="iso-8859-1"
>
> It may not work for you depending on your specific problem constraints, but
> if you could flatten the arrays, then it would be a dot, and you could
> maybe
> compute multiple such dot products by storing those flattened arr
Hi,
Thanks for getting back to me.
I'm doing element wise multiplication, basically innerProduct =
numpy.sum(array1*array2) where array1 and array2 are, in general,
multidimensional. I need to do many of these operations, and I'd like to
split up the tasks between the different cores. I'm not using
Hello all,
Since I haven't heard back, I decided to simplify my problem even more. I'm
attaching two files which demonstrate the problem very clearly. My original
message is below. The script can be run with "python shared_mem.py".
Thanks,
Brandt
> > Hello,
> > I'm parallelizing some code I've wr
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Hello,
I'm parallelizing some code I've written using the built in multiprocessing
module. In my application, I need to multiply many large arrays together and
sum the resulting product arrays (inner products). I noticed that when I
parallelized this with myPool.map(...) with 8 processes (on an 8-c
Contents of NumPy-Discussion digest..."
>
>
> Today's Topics:
>
> 1. Re: Largest possible numpy array (Sturla Molden)
> 2. Where can I download numpy.i? (Brandt Belson)
> 3. Re: Where can I download numpy.i? (Bill Spotz)
>
>
> ---
I just want to know exactly how to get the file numpy.i (for SWIG), I've
come across links that seem to take me nowhere.
Thank you.
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Hello,
I'm not sure if f2py questions are appropriate here, but I have a question.
I had been using f2py without problems, but recently it stopped working.
It's worth mentioning that I'm working remotely on a cluster, and I don't
have root access, so it's possible that the system admins changed som
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