[SciPy-dev] FFTW performances in scipy and numpy

[David Cournapeau]

John Travers wrote:
> On 01/08/07, David Cournapeau <david at ar.media.kyoto-u.ac.jp> wrote:
>   
>> Anne Archibald wrote:
>>     
>>> On 01/08/07, David Cournapeau <david at ar.media.kyoto-u.ac.jp> wrote:
>>>
>>>       
>>>>     I am one of the contributor to numpy/scipy. Let me first say I am
>>>> *not* the main author of the fftw wrapping for scipy, and that I am a
>>>> relatively newcommer in scipy, and do not claim a deep understanding of
>>>> numpy arrays. But I have been thinking a bit on the problem since I am a
>>>> big user of fft and debugged some problems in the scipy code since.
>>>>         
>> Ok, I prepared a small package to test several strategies:
>>
>> http://www.ar.media.kyoto-u.ac.jp/members/david/archives/fftdev.tbz2
>>
>> By doing make test, it should build out of the box and run the tests (if
>> you are on Linux, have gcc and fftw3, of course :) ). I did not even
>> check whether the computation is OK (I just tested against memory
>> problems under valgrind).
>>
>> 3 strategies are available:
>>     - Have a flag to check whether the given array is 16 bytes aligned,
>> and conditionnally build plans using this info
>>     - Use FFTW_UNALIGNED, and do not care about alignement
>>     - Current strategy: copy.
>>
>> The three strategies use FFTW_MEASURE, which I didn't do before, and may
>>     
>
> Another strategy worth trying is using FFTW_MEASURE once and then
> using FFTW_ESTIMATE for additional arrays. FFTW accumulates wisdom and
> so the initial call with MEASURE means that further estimated plans
> also benefit. In my simple tests it comes very close to measuring for
> each individual array.
>   
Is this true for different arrays size ?

David