Quote:
Originally Posted by ewmayer
A more precise estimate, which also shows how the accuracy decreases (quite slowly though) with increasing FFT length can be found here  two out of three authors of that paper have posted to this thread, so hey, it must be true. ;)

That's why I came here to ask.
FWIW, I switched to a balanceddigit representation, and now it works through 2^22, at least when testing the primes M11213, M21701, M44497 and M86243.
I now understand the cause of that limit...the depth of the FFT requires more additions of values, and I had been thinking in terms of decimal, not binary digits when I said it should only account for a couple of digits of precision. I expect that the loss of precision should grow *much* more slowly beyond the numbers I've already tested (up to the 34th Mersenne prime).
My next optimization will be to convert to a realvalued FFT, hopefully cutting the FFT size for most exponents in half, but since the data will be twice as dense, I expect to lose that bit I gained going to the balanceddigit representation.
Thanks,
Drew