[SciPy-User] fast small matrix multiplication with cython?
josef.pktd@gmai...
josef.pktd@gmai...
Tue Dec 7 11:05:21 CST 2010
On Tue, Dec 7, 2010 at 11:53 AM, Charles R Harris
<charlesr.harris@gmail.com> wrote:
>
>
> On Tue, Dec 7, 2010 at 9:47 AM, Skipper Seabold <jsseabold@gmail.com> wrote:
>>
>> On Tue, Dec 7, 2010 at 9:54 AM, Charles R Harris
>> <charlesr.harris@gmail.com> wrote:
>> >
>> >
>> > On Mon, Dec 6, 2010 at 11:56 PM, Fernando Perez <fperez.net@gmail.com>
>> > wrote:
>> >>
>> >> Hi Skipper,
>> >>
>> >> On Mon, Dec 6, 2010 at 2:34 PM, Skipper Seabold <jsseabold@gmail.com>
>> >> wrote:
>> >> > I'm wondering if anyone might have a look at my cython code that does
>> >> > matrix multiplication and see where I can speed it up or offer some
>> >> > pointers/reading. I'm new to Cython and my knowledge of C is pretty
>> >> > basic based on trial and (mostly) error, so I am sure the code is
>> >> > still very naive.
>> >>
>> >> a few years ago I had a similar problem, and I ended up getting a very
>> >> significant speedup by hand-coding a very unsafe, but very fast pure C
>> >> extension just to compute these inner products. This was basically a
>> >> replacement for dot() that would only work with double precision
>> >> inputs of compatible dimensions and would happily segfault with
>> >> anything else, but it ran very fast. The inner loop is implemented
>> >> completely naively, but it still beats calls to BLAS (even linked with
>> >> ATLAS) for small matrix dimensions (my case was also up to ~ 15x15).
>> >>
>> >> I'm attaching the code in case you find it useful, please keep in mind
>> >> I haven't compiled it in years, so it may have bit-rotted a little.
>> >>
>> >
>> > Blas adds quite a bit of overhead for multiplying small matrices, but so
>> > does calling from python. For implementing Kalman filters it might be
>> > better
>> > to write a whole Kalman class so that operations can be combined at the
>> > c
>> > level.
>> >
>> > Skipper, what kind of Kalman filter are you trying to implement?
>> >
>>
>> It's just a linear Gaussian filter. I use it to get the loglikelihood
>> of a univariate ARMA series with exact initial conditions. As it
>> stands it is fairly inflexible, but if I can make it fast I would like
>> to generalize it.
>>
>> There is a fair amount of scratch work in here, and some attempts at
>> generalized state space models, but all the action for my purposes is
>> in KalmanFilter.loglike
>>
>>
>> http://bazaar.launchpad.net/~jsseabold/statsmodels/statsmodels-skipper/annotate/head%3A/scikits/statsmodels/tsa/kalmanf/kalmanf.py#L505
>>
>> It's not terribly slow, but I have to maximize the likelihood using
>> numerical derivatives, so it's getting called quite a few times. A
>> 1000 observation ARMA(2,2) series takes about 5-6 seconds on my
>> machine with fmin_l_bfgs_b.
>>
>
> Just a guess here, but the numerical derivative bit makes it sounds like you
> are implementing a generalized Kalman filter. Have you looked at unscented
> Kalman filters?
It's still a linear filter, non-linear optimization comes in because
the exact loglikelihood function for ARMA is non-linear in the
coefficients.
(There might be a way to calculate the derivative in the same loop,
but that's a different issue.)
Josef
>
> Chuck
>
>
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