[SciPy-User] fast small matrix multiplication with cython?
Skipper Seabold
jsseabold@gmail....
Thu Dec 9 15:33:41 CST 2010
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.
Skipper
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