Hi Simon,
Thanks for your help, it worked correctly. However the reduction in
the time is not very significantly
(1/6) of the total (~300 secs). I think my arrays are too big
(6000000-element Array{Float64,1})
to use PATIENT or even MEASURE, since that MATLAB spends just 60 secs.
2015-06-15 8:24 GMT-04:00 Simon Byrne <[email protected]>:
> Hi Juan,
>
> You would also need to change Complex64 to Complex128.
>
> -Simon
>
>
> On Sunday, 14 June 2015 22:25:21 UTC+1, Juan Carlos Cuevas Bautista wrote:
>>
>> Hi Everybody,
>>
>> I am new in Julia Programming. I have been following this post to optimize
>> my code of time series in which I have big heavy files of data. I changed
>> the line
>> float32([1:38192]) by float64([1:38192]) but the script does not work,
>> I know this is related with this method
>>
>> Base.FFTW.Plan(ri, ro, 1, Base.FFTW.PATIENT, Base.FFTW.NO_TIMELIMIT),
>>
>> I was wondering if this just work for float32 arrays? In case to be
>> positive
>> could anyone recommend me another way to optimize the fft routine?
>>
>> Thank you so much and I like so much Julia.
>>
>>
>> On Monday, November 4, 2013 at 6:50:14 PM UTC-5, SYoon wrote:
>>>
>>> I am trying to optimize FFT performance following the suggestion in the
>>> discussion .
>>> I have the following testing function:
>>>
>>> function test()
>>> a = float32([1:38192])
>>> b = Array(Complex64, length(a))
>>>
>>> tic()
>>> for i = 1:100
>>> b = fft(a)
>>> end
>>> toc();
>>> end
>>>
>>>
>>> This takes about 0.146 seconds (Matlab takes about 0.06 seconds)
>>>
>>>
>>> Now, I have implemented three helper functions:
>>>
>>>
>>> function get_fft_plan(ri)
>>> ro = Array(Complex64, length(ri)>>1 + 1)
>>> r2c = Base.FFTW.Plan(ri, ro, 1, Base.FFTW.PATIENT,
>>> Base.FFTW.NO_TIMELIMIT)
>>> return ro, r2c
>>> end
>>>
>>> function execute_fft_plan(ri, ro, r2c, symmetry)
>>> Base.FFTW.execute(r2c.plan, ri, ro)
>>>
>>> if ~(symmetry)
>>> return ro
>>> else symmetry
>>> roo = make_fft_symmetry(ro)
>>> return roo
>>> end
>>> end
>>>
>>> function make_fft_symmetry(ro)
>>> len = 2*(length(ro) - 1)
>>> roo = Array(Complex64, len)
>>> roo[1] = ro[1]
>>>
>>> for i = 2:len/2+1
>>> roo[i] = conj(ro[i])
>>> roo[end-i+2] = ro[i]
>>> end
>>> return roo
>>> end
>>>
>>>
>>> Now, my updated test function looks like
>>>
>>>
>>> function test()
>>> a = float32([1:38192])
>>> b = Array(Complex64, length(a))
>>> ro, r2c = get_fft_plan(a)
>>>
>>> tic()
>>> for i = 1:100
>>> b = execute_fft_plan(a, ro, r2c, true)
>>> end
>>> toc();
>>> end
>>>
>>> This takes about 0.074 seconds. When I change "true" to "false", it takes
>>> about 0.053 seconds. So, it takes about 0.02 seconds longer to make the FFT
>>> output array full length. Can somebody suggest a better way to implement the
>>> function "make_fft_symmetry" so that 0.074 seconds becomes closer to Matlab
>>> performance (0.06 seconds) or better?
>>>
>>> Thanks.
