[SciPy-User] fast small matrix multiplication with cython?

Skipper Seabold jsseabold@gmail....
Thu Dec 9 16:01:55 CST 2010


On Thu, Dec 9, 2010 at 4:33 PM, Skipper Seabold <jsseabold@gmail.com> wrote:
> On Wed, Dec 8, 2010 at 11:28 PM,  <josef.pktd@gmail.com> wrote:
>>>
>>> It looks like I don't save too much time with just Python/scipy
>>> optimizations.  Apparently ~75% of the time is spent in l-bfgs-b,
>>> judging by its user time output and the profiler's CPU time output(?).
>>>  Non-cython versions:
>>>
>>> Brief and rough profiling on my laptop for ARMA(2,2) with 1000
>>> observations.  Optimization uses fmin_l_bfgs_b with m = 12 and iprint
>>> = 0.
>>
>> Completely different idea: How costly are the numerical derivatives in l-bfgs-b?
>> With l-bfgs-b, you should be able to replace the derivatives with the
>> complex step derivatives that calculate the loglike function value and
>> the derivatives in one iteration.
>>
>
> I couldn't figure out how to use it without some hacks.  The
> fmin_l_bfgs_b will call both f and fprime as (x, *args), but
> approx_fprime or approx_fprime_cs need actually approx_fprime(x, func,
> args=args) and call func(x, *args).  I changed fmin_l_bfgs_b to make
> the call like this for the gradient, and I get (different computer)
>
>
> Using approx_fprime_cs
> -----------------------------------
>         861609 function calls (861525 primitive calls) in 3.337 CPU seconds
>
>   Ordered by: internal time
>
>   ncalls  tottime  percall  cumtime  percall filename:lineno(function)
>       70    1.942    0.028    3.213    0.046 kalmanf.py:504(loglike)
>   840296    1.229    0.000    1.229    0.000 {numpy.core._dotblas.dot}
>       56    0.038    0.001    0.038    0.001 {numpy.linalg.lapack_lite.zgesv}
>      270    0.025    0.000    0.025    0.000 {sum}
>       90    0.019    0.000    0.019    0.000 {numpy.linalg.lapack_lite.dgesdd}
>       46    0.013    0.000    0.014    0.000
> function_base.py:494(asarray_chkfinite)
>      162    0.012    0.000    0.014    0.000 arima.py:117(_transparams)
>
>
> Using approx_grad = True
> ---------------------------------------
>         1097454 function calls (1097370 primitive calls) in 3.615 CPU seconds
>
>   Ordered by: internal time
>
>   ncalls  tottime  percall  cumtime  percall filename:lineno(function)
>       90    2.316    0.026    3.489    0.039 kalmanf.py:504(loglike)
>  1073757    1.164    0.000    1.164    0.000 {numpy.core._dotblas.dot}
>      270    0.025    0.000    0.025    0.000 {sum}
>       90    0.020    0.000    0.020    0.000 {numpy.linalg.lapack_lite.dgesdd}
>      182    0.014    0.000    0.016    0.000 arima.py:117(_transparams)
>       46    0.013    0.000    0.014    0.000
> function_base.py:494(asarray_chkfinite)
>       46    0.008    0.000    0.023    0.000 decomp_svd.py:12(svd)
>       23    0.004    0.000    0.004    0.000 {method 'var' of
> 'numpy.ndarray' objects}
>
>
> Definitely less function calls and a little faster, but I had to write
> some hacks to get it to work.
>

This is more like it!  With fast recursions in Cython:

         15186 function calls (15102 primitive calls) in 0.750 CPU seconds

   Ordered by: internal time

   ncalls  tottime  percall  cumtime  percall filename:lineno(function)
       18    0.622    0.035    0.625    0.035
kalman_loglike.pyx:15(kalman_loglike)
      270    0.024    0.000    0.024    0.000 {sum}
       90    0.019    0.000    0.019    0.000 {numpy.linalg.lapack_lite.dgesdd}
      156    0.013    0.000    0.013    0.000 {numpy.core._dotblas.dot}
       46    0.013    0.000    0.014    0.000
function_base.py:494(asarray_chkfinite)
      110    0.008    0.000    0.010    0.000 arima.py:118(_transparams)
       46    0.008    0.000    0.023    0.000 decomp_svd.py:12(svd)
       23    0.004    0.000    0.004    0.000 {method 'var' of
'numpy.ndarray' objects}
       26    0.004    0.000    0.004    0.000 tsatools.py:109(lagmat)
       90    0.004    0.000    0.042    0.000 arima.py:197(loglike_css)
       81    0.004    0.000    0.004    0.000
{numpy.core.multiarray._fastCopyAndTranspose}

I can live with this for now.

Skipper


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