What is the optimal size of block in the overlap-add method?

[NimaSarajpoor]

In Tutorial_FFT_Based_SDP.ipynb. the overlap-add method is explained. This method performs several independent circular convolution on the blocks of array. However, it does not provide any guidance on how to find an optimal size of block. It should be a good idea to do an analysis.

[seanlaw]

I know you are referring to the tutorial but, for the actual code,Is it possible/sufficient to simply call a SciPy function to obtain the optimal block size?

[NimaSarajpoor]

Is it possible/sufficient to simply call a SciPy function to obtain the optimal block size?

It is, and this issue should be treated as low-priority.

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That scipy's function is _calc_oa_lens and its logic is actually used in PR #35 (as a side note: better to just use that function directly if possible rather than using its pieces. Need to check impact.)

The scipy's approach is based on the Eq.3 in the overlap-add method page on wikipedia. And, analyzing that "equation" might unlock an opportunity for boosting the performance.

[NimaSarajpoor]

Regarding the size of block in overlap-add:

That scipy's function is _calc_oa_lens and its logic is actually used in PR #35 (as a side note: better to just use that function directly if possible rather than using its pieces. Need to check impact.)

It turns out that using the existing function _calc_oa_lens results in a considerable performance hit. The reason seems to be related to the fact that this function does not consider the type of input (real or complex) when computing next_fast_len. The impact of passing the default real=False in block_size = sp_fft.next_fast_len(math.ceil(opt_size)) is shown in the figure shared in this comment