By Arndt J.

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8 for the backward transform. The complex squarings are then combined with the pre- and postprocessing steps, thereby interleaving the most nonlocal memory accesses with several arithmetic operations. 4 Complex FT by complex HT and vice versa A complex valued HT is simply two HTs (one of the real, one of the imag part). 8 and there is nothing new. Really? 8 equivalent is hopefully obvious) This may not make you scream but here is the message: it makes sense to do so. e. usual) version1 and with a well optimized FHT you get an even better optimized FFT.

X + y = n + τ ) and those where simply x + y = τ holds. These are (following the notation in [18]) denoted by h(1) and h(0) respectively. 7) where h(0) = ax bτ −x x≤τ h(1) = ax bn+τ −x x>τ There is a simple way to seperate h(0) and h(1) as the left and right half of a length-2 n sequence. This is just what the acyclic (or linear) convolution does: Acyclic convolution of two (length-n) sequences a and b can be defined as that length-2 n sequence h which is the cyclic convolution of the zero padded sequences A and B: A := {a0 , a1 , a2 , .

4. Transpose the matrix. Note the elegance! 8 (transposed matrix Fourier algorithm) The (TMFA) for the FFT: transposed matrix Fourier algorithm 1. Transpose the matrix. 2. Apply a (length C) FFT on each column (transposed row). 3. Multiply each matrix element (index r, c) by exp(±2 π i r c/n). 4. Apply a (length R) FFT on each row (transposed column). e. g. in unit strides). In radix 2 (or 2n ) algorithms one even has skips of powers of 2, which is particularly bad on computer systems that use direct mapped cache memory: One piece of cache memory is responsible for caching addresses that lie apart by some power of 2.