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

Hi all, At Continuum we are trying to coordinate with Intel about releasing our patches from Accelerate upstream as well rather than having them redo things we have already done but have just not been able to open source yet. Accelerate also uses GPU accelerated FFTs and it would be nice if

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

On Jun 1, 2016 4:47 PM, "David Cournapeau" wrote: > > > > On Tue, May 31, 2016 at 10:36 PM, Sturla Molden wrote: >> >> Joseph Martinot-Lagarde wrote: >> >> > The problem with FFTW is that its license is more restrictive (GPL),

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

On Tue, May 31, 2016 at 10:36 PM, Sturla Molden wrote: > Joseph Martinot-Lagarde wrote: > > > The problem with FFTW is that its license is more restrictive (GPL), and > > because of this may not be suitable everywhere numpy.fft is. > > A lot of us

[Numpy-discussion] ANN: numexpr 2.6.0 released

= Announcing Numexpr 2.6.0 = Numexpr is a fast numerical expression evaluator for NumPy. With it, expressions that operate on arrays (like "3*a+4*b") are accelerated and use less memory than doing the same calculation in Python. It wears

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

Seems so. numpy/fft/__init__.py when installed with conda contains a thin optional wrapper around mklfft, e.g. this here: https://docs.continuum.io/accelerate/mkl_fft It is part of the accelerate package from continuum and thus not free. Cheers! Lion On 01/06/16 09:44, Gregor Thalhammer

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

> Am 31.05.2016 um 23:36 schrieb Sturla Molden : > > Joseph Martinot-Lagarde wrote: > >> The problem with FFTW is that its license is more restrictive (GPL), and >> because of this may not be suitable everywhere numpy.fft is. > > A lot of us use