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. _______________________________________________ PyCUDA mailing list PyCUDA@tiker.net http://lists.tiker.net/listinfo/pycuda