Hello, I'd like to do an FFT of a moderately large 3D cube, 1024^3. Looking at the run-time of smaller arrays, this is not a problem in terms of compute time, but the array doesn't fit in memory. So, several questions:
1) Numerical Recipes has an out-of-memory FFT algorithm, but looking through the numpy and scipy docs and modules, I didn't find a function that does the same thing. Did I miss it? Should I get to work typing it in? 2) I had high hopes for just memory-mapping the large array and passing it to the standard fft function. However, the memory-mapped region must fit into the address space, and I don't seem to be able to use more than 2 GB at a time. So memory mapping doesn't seem to help me at all. This last issue leads to another series of things that puzzle me. I have an iMac running OS X 10.5 with an Intel Core 2 duo processor and 4 GB of memory. As far as I've learned, the processor is 64 bit, the operating system is 64 bit, so I should be able to happily memory-map my entire disk if I want. However, Python seems to run out of steam when it's used 2 GB. This is true of both 2.5 and 2.6. What gives? Is this a Python issue? Thanks, Greg _______________________________________________ Numpy-discussion mailing list Numpy-discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion