Received from Jerome Kieffer on Tue, May 29, 2012 at 04:17:52PM EDT:
> Dear PyCuda community,
>
> First of all I would like to introduce myself: I am a scientific
> developer and I am pretty new to PyCuda (even if I followed a CUDA
> course). I would like to port part of a very big application to GPU,
> switching from FFTw to scikit.cuda (cu_fft part). This was straight
> forward, thanks to the very good abstraction done in PyCuda. I got
> already speed-up of 5x with exactly the same result compared to fftw.
>
> My problems starts when integrating the code into python-threads;
> indeed the large application will make all PyCuda calls from different
> threads and ends with memory leaks on the GPU crashing after a couple
> of minutes. So I need to enforce all python threads to use the same
> context on the GPU.
>
> I have another question: why is the data1_gpu.ptr changing whereas
> data2_gpu.ptr and plan fixed (as expected in my code ?
The address changes because the fft() and ifft() functions effectively
allocate a new array when scaling of the result is requested. I'll
modify the functions to perform the scaling operation in-place.
L.G.
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