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

Sebastian Haase seb.haase@gmail....
Tue Feb 15 10:19:45 CST 2011


Thanks Matthieu,
using __restrict__ with g++ did not change anything. How do I use
valgrind with C extensions?
I don't know what "PAPI profil" is ...?
-Sebastian


On Tue, Feb 15, 2011 at 4:54 PM, Matthieu Brucher
<matthieu.brucher@gmail.com> wrote:
> Hi,
> My first move would be to add a restrict keyword to dist (i.e. dist is the
> only pointer to the specific memory location), and then declare dist_ inside
> the first loop also with a restrict.
> Then, I would run valgrind or a PAPI profil on your code to see what causes
> the issue (false sharing, ...)
> Matthieu
>
> 2011/2/15 Sebastian Haase <seb.haase@gmail.com>
>>
>> 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).
>> 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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>
>
>
> --
> Information System Engineer, Ph.D.
> Blog: http://matt.eifelle.com
> LinkedIn: http://www.linkedin.com/in/matthieubrucher
>
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