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Changes between Version 22 and Version 23 of Examples/Fft2dHighpass

v22	v23
48	48	The vector version uses the same recursive radix-2 decimation in time (DIT) algorithm as the Repa version, but is not rank generalised. It applies a recursive 1d FFT to each row and then transposes the matrix, twice each. Recursive FFT algorithms tend to be slower than in-place ones because intermediate data is written into new vectors at each recursion. A 512 point FFT is built from two 256 point FFTs, which are built from 4 128 point FFTs and so on. The result of each FFT is a new vector which needs to be allocated, filled, then unboxed again during the next recursion. The base case is a 2 point vector, so there are 256 of them to allocate then unbox at the lowest step.
49	49
50		Jones's version also uses a 1d radix-2 DIT FFT kernel, but it first reorders the values then performs a in-place transform using three nested loops. Using an in-place algorithm gives better locality, and avoids the need to allocate and unbox all the intermediate vectors.
	50	Jones's version also uses a 1d radix-2 DIT FFT kernel, but it first reorders the values then performs an in-place transform using three nested loops. Using an in-place algorithm gives better locality, and avoids the need to allocate and unbox all the intermediate vectors.
51	51
52	52	FFTW contains deep magic, and is comparable with vendor optimised versions.