If you are working over floating point, you probably with to use Estrin's method (see https://en.wikipedia.org/wiki/Estrin%27s_scheme - also tab 3 on the website.)

It takes advantage of FMA (fused multiply add), has good numeric stability and uses pipelining optimally.

A while ago I suggested using Estrin's method in Boost, for functions like std::exp. There's some interesting discussions here: https://github.com/boostorg/math/issues/924 if you are interested in all the practical details.

However, for finite fields (e.g. used for hashing and cryptography) multiplication is much more expensive than addition, which is the main use of this algorithm.

In modern computers, the throughput of an execution unit is the same for multiplications and additions, but multiplication frequently has a greater latency, by 1 or 2 clock cycles.

Many CPUs, like the AMD Zen CPUs, have more execution units that can do additions, than those that can do multiplications. So the aggregated throughput over all execution units can be higher for additions than for multiplications.

For example, for floating-point numbers, the AMD Zen CPUs have 4 vector execution units, where all 4 can do additions, but only 2 of them can do multiplications or fused multiply-add operations. So Zen CPUs can do up to 4 additions + 2 multiplications per clock cycle (when 2 multiplication-addition pairs are fused).

Just a few years ago, mults were slower, but I think now (Intel i9) mult, add and fma are the same.

https://stackoverflow.com/a/39135689

I think multiplications are faster to do in computer land than adds? I too am curious.