The STOC 2008 paper "Fast Integer Multiplication using Modular Arithmetic" by De et al http://arxiv.org/abs/0801.1416 shows how to use $p$-adic numbers instead of $\mathbb C$ used in Furer's multiplication algorithm. Given that Furer's algorithm is not practical for less than a few thousand bits (is there a good estimate of its breakeven point vs Toom-Cook and other algorithms?), I wonder if the De et al algorithm has a lower breakeven point, is easier to implement, and is more practical in general.

Pointers to follow-up work and/or attempts at implementing the STOC 2008 algorithm would be appreciated.


  • $\begingroup$ Is any FFT base algorithm practical for less than a few thousand bits? $\endgroup$
    – Igor Rivin
    Aug 4, 2012 at 22:02
  • $\begingroup$ Apparently not - the Toom-Cook family of algorithms rules in a large range. This old paper lyle.smu.edu/~seidel/courses/cse8351/papers/ZurasMult.pdf evaluates Schonhage-Strassen and claims that it does not win at least until 37M bits $\endgroup$ Aug 5, 2012 at 1:33

1 Answer 1


For new results, in integer multiplication, check the breakthrough paper by: David Harvey, and Joris Van Der Hoeven, et al . Integer multiplicaion in $O(n*(log$ $n))$. This proves Schonhage Strassens' conjecture from the 1970s that integer multiplication is really possible in $O(n*(log$ $n))$. From, straightforward (school) integer multiplication which is $O( n^{2}) $, to karatsubas' algorithm which is $O(n^{1.58})$, to $O(n*(log$ $n))$, by above authors. The authors use the property of specific multivariate polynomial rings that admit efficient multiplication.

The authors show that integer multiplication (which is one dimensional) could be represented in a setting of a specific multivariate polynomial ring. Starting with a binary representation of integers, begin with the fixed point coordinate vectors(to a precision), and then go on to utilize them in coefficient rings for that polynomial representation. One could select parameters, and reduce the integer multiplication problem to one of convolution over a ring with a specific structure, reaching the bound.

The bound is a significant improvement over earlier algorithms, and with many digits the efficiencies are apparent, for a large number of digits billions, and larger scales as author(s) claim its unknown, but good performance is possible.

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    $\begingroup$ While of theoretical interest, the method is staggeringly not practical in its current form. According to GLL 'they note that their estimates take effect for $n\geq 2^{d^{12}}$ where they use $d=1729$'. The appearance of the number $1729$ is not coincidental. $\endgroup$ Apr 8, 2019 at 5:50
  • $\begingroup$ This does not answer the question as stated in the body of the question, which is about practicality and implementations of fast multiplication algorithms. $\endgroup$
    – Wojowu
    Apr 8, 2019 at 8:30

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