[Numpy-discussion] OT: performance in C extension; OpenMP, or SSE ?

Chris Colbert sccolbert@gmail....
Tue Feb 15 19:28:38 CST 2011


The `cdist` function in scipy spatial does what you want, and takes ~ 1ms on
my machine.


In [1]: import numpy as np

In [2]: from scipy.spatial.distance import cdist

In [3]: a = np.random.random((340, 2))

In [4]: b = np.random.random((329, 2))

In [5]: c = cdist(a, b)

In [6]: c.shape
Out[6]: (340, 329)

In [7]: %timeit cdist(a, b)
1000 loops, best of 3: 1.18 ms per loop



On Tue, Feb 15, 2011 at 4:42 PM, Sebastian Haase <seb.haase@gmail.com>wrote:

> Hi Eat,
> I will surely try these routines tomorrow,
> but I still think that neither scipy function does the complete
> distance calculation of all possible pairs as done by my C code.
> For 2 arrays, X and Y, of nX and nY 2d coordinates respectively, I
> need to get nX times nY distances computed.
> >From the online documentation I understand that
> pdist calculates something like nX square distances for a single array
> X of nX coordinates,
> and cdist would calculate nX distances, where nX is required to equal nY.
>
> Thanks for looking into this,
> Sebastian
>
> On Tue, Feb 15, 2011 at 9:11 PM, eat <e.antero.tammi@gmail.com> wrote:
> > Hi,
> >
> > On Tue, Feb 15, 2011 at 5:50 PM, Sebastian Haase <seb.haase@gmail.com>
> > wrote:
> >>
> >> Hi,
> >> I assume that someone here could maybe help me, and I'm hoping it's
> >> not too much off topic.
> >> I have 2 arrays of 2d point coordinates and would like to calculate
> >> all pairwise distances as fast as possible.
> >> Going from Python/Numpy to a (Swigged) C extension already gave me a
> >> 55x speedup.
> >> (.9ms vs. 50ms for arrays of length 329 and 340).
> >
> > With my very modest machine (Intel Dual CPU E2200, 2.2 Ghz) utilizing
> > scipy.spatial.distance.pdist will take some 1.5 ms for such arrays. Even
> the
> > simple linear algebra based full matrix calculations can be done less
> than 5
> > ms.
> >
> > My two cents,
> > eat
> >>
> >> I'm using gcc on Linux.
> >> Now I'm wondering if I could go even faster !?
> >> My hope that the compiler might automagically do some SSE2
> >> optimization got disappointed.
> >> Since I have a 4 core CPU I thought OpenMP might be an option;
> >> I never used that, and after some playing around I managed to get
> >> (only) 50% slowdown(!) :-(
> >>
> >> My code in short is this:
> >> (My SWIG typemaps use obj_to_array_no_conversion() from numpy.i)
> >> -------<Ccode> ----------
> >> void dists2d(
> >>                   double *a_ps, int nx1, int na,
> >>                   double *b_ps, int nx2, int nb,
> >>                   double *dist, int nx3, int ny3)  throw (char*)
> >> {
> >>  if(nx1 != 2)  throw (char*) "a must be of shape (n,2)";
> >>  if(nx2 != 2)  throw (char*) "b must be of shape (n,2)";
> >>  if(nx3 != nb || ny3 != na)    throw (char*) "c must be of shape
> (na,nb)";
> >>
> >>  double *dist_;
> >>  int i, j;
> >>
> >> #pragma omp parallel private(dist_, j, i)
> >>  {
> >> #pragma omp for nowait
> >>        for(i=0;i<na;i++)
> >>          {
> >>                //num_threads=omp_get_num_threads();  --> 4
> >>                dist_ = dist+i*nb;                 // dists_  is  only
> >> introduced for OpenMP
> >>                for(j=0;j<nb;j++)
> >>                  {
> >>                        *dist_++  = sqrt( sq(a_ps[i*nx1]   - b_ps[j*nx2])
> +
> >>
>  sq(a_ps[i*nx1+1]
> >> - b_ps[j*nx2+1]) );
> >>                  }
> >>          }
> >>  }
> >> }
> >> -------</Ccode> ----------
> >> There is probably a simple mistake in this code - as I said I never
> >> used OpenMP before.
> >> It should be not too difficult to use OpenMP correctly here
> >>  or -  maybe better -
> >> is there a simple SSE(2,3,4) version that might be even better than
> >> OpenMP... !?
> >>
> >> I supposed, that I did not get the #pragma omp lines right - any idea ?
> >> Or is it in general not possible to speed this kind of code up using
> >> OpenMP !?
> >>
> >> Thanks,
> >> Sebastian Haase
> >> _______________________________________________
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> >> http://mail.scipy.org/mailman/listinfo/numpy-discussion
> >
> >
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