| Re: Is it possible to speed up the Interpolate command? [message #89254 is a reply to message #89242] |
Fri, 08 August 2014 16:18  |
Stephen Messenger
Messages: 3
Registered: August 2014
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Junior Member |
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On Thursday, August 7, 2014 8:42:44 PM UTC-4, Mike Galloy wrote:
> On 8/7/14, 4:11 pm, sjm7w6@gmail.com wrote:
>
>> I've been stuck on figuring out how to speed up an interpolation
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>> calculation and wondered if anyone has any suggestions?
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>>
>
>> Here's the situation:
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>>
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>> I have a bunch (about 450,000) of 2d matrices that I need to
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>> interpolate within. Within each of the matrices, I'm looking to
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>> interpolate for 100 x/y combinations where I want values at points
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>> (x_1, y_1), (x_2, y_2), etc. (I am not looking to regrid the data,
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>> i.e., I don't need x_1,y_2). The matrices are currently stacked in a
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>> datacube (dimensions are 14 x 28 x 450000). Each of the matrices has
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>> the same x/y locations for the points to be interpolated. I thereby
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>> use "interpolate" to interpolate each matrix for the 100 values and
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>> then loop over the 3rd dimension. This utilizes the bilinear
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>> interpolation. Though, I have the matrices stacked in the data cube,
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>> I do not want a trilinear interpolation as the 3 dimension is
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>> independent. Here's the current code:
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>>
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>> for i=0, numlines-1 do begin
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>> Values(*,i)=interpolate(datacube(*,*,i),x_loc,y_loc) endfor
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>>
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>> Numlines is the n_elements(3rd dimension), which is the 450,000
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>> referenced above. The x and y dimensions of the data cube are 14 and
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>> 28, respectively..
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>>
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>> The interpolation is taking about 2 seconds to run. I'm looking to
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>> find a way to trim it as much as possible...hopefully less than 0.1
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>> seconds. This may be difficult given that the interpolation is
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>> calculating 45,000,000 values.
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>>
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>> Things I've tried: 1) I first removed the interpolation from the for
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>> loop. However, the combination of that interpolation with reforming
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>> the output result into the matrix I need requires this process to
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>> actually take longer than the for loop above...this provides evidence
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>> the existence of the for loop is not the rate limiting step.
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>>
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>> 2) I rearranged the datacube into a very large 2d matrix (basically
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>> stacking in the 2nd dimension as opposed to creating the 3rd
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>> dimension). This lead to the same calculation time as the original
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>> way above, so no gains there..
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>>
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>> I need a bilinear interpolation due to the first two dimensions being
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>> linked so interpol will not work. I do not need regridded data, so I
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>> don't think that Krig2d or bilinear offer any help.
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>>
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>> I know that I can speed up the code by simply decreasing the size of
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>> the 3rd dimension and/or by interpolating for less than 100 values
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>> per matrix, but I'm trying to avoid this.
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>>
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>> Any suggestions on how to calculate this faster?
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>>
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>> Thank you for your time.
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>>
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>
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> You could try GPULib; it has a GPU accelerated interpolation routine.
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> Demo at: http://www.txcorp.com/home/gpulib. (Full disclosure: I am the
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> product manager for GPULib.) It only does bilinear interpolation, but it
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> sounds like you just want to do a stack of bilinear interpolations.
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>
>
> Mike
>
> --
>
> Michael Galloy
>
> www.michaelgalloy.com
>
> Modern IDL: A Guide to IDL Programming (http://modernidl.idldev.com)
>
> Research Mathematician
>
> Tech-X Corporation
Hi Mike,
I'm going to check if that will work for me. Thank you for that suggestion.
Thanks!
Stephen
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