[Numpy-discussion] Changing FFT cache to a bounded LRU cache

[Lion Krischer]

Hi all,

I was told to take this to the mailing list. Relevant pull request:
https://github.com/numpy/numpy/pull/7686


NumPy's FFT implementation caches some form of execution plan for each
encountered input data length. This is currently implemented as a simple
dictionary which can grow without bounds. Calculating lots of different
FFTs thus cause a memory leak from the users' perspective. We
encountered a real world situation where this is an issue.

The PR linked above proposes to replace the simple dictionary with an
LRU (least recently used) cache. It will remove the least recently used
pieces of data if it grows beyond a specified size (currently an
arbitrary limit of 100 MB per cache). Thus almost all users will still
benefit from the caches but their total memory size is now limited.


Things to consider:

* This adds quite some additional complexity but I could not find a
simple way to achieve the same result.
* What is a good limit on cache size? I used 100 MB because it works for
my uses cases.


Cheers!

Lion