A faster approximation to floating point power, at the trade-off of some accuracy. While Julia's built-in floating point ^ tries to achieve ~1ulp accuracy, this version of floating point power approximation achieves much fewer digits of accuracy (approximately 10 digits of accuracy) while being much faster. This is developed as a library in order to make the choice to opt-in as a replacement to ^ very easy but deliberate on the side of the user.

using Pkg
Pkg.add("FastPower")

Using FastPower.jl is dead simple: instead of x^y, do the following:

using FastPower
FastPower.fastpower(x,y)

That's it. That's all there is.

The name simply derives from the Julia standard of x_fast for things that are approximations. FastPower is simply the the ^_fast or pow_fast function, following the standard conventions developed from Base. However, this differs from the pow_fast you get from Base which is still a lot more accurate. FastPower.fastpower loses about 12 digits of accuracy on Float64, so it's about 3-4 digits of accuracy. For many applications, such as the adaptivity algorithm when solving differential equations, this can be a sufficient amount of accuracy for a power function approximation.

This approximation is tested for the range of x^y where x>=0 and y>=0. If x<=1, then the approximation is accurate for very large values of y. If x<100, then the approximation is accurate for y<1. If x>100, or if x is large and y is large, caution should be used.

These two packages are completely unrelated since FastPow.jl is a specialization for literal integer powers: powers that are not only integers but appear as literal constants in the source code. It does things like:

x^5

which is transformed by the @fastpow macro to be computed via:

sq = x^2
fourth = sq^2
fourth * 2

This is faster than ^(::AbstractFloat, Integer) but with a bit of accuracy loss compared to what LLVM generates by default for x^5.

Meanwhile, FastPower.jl is all about floating-point powers (whose value may only be known at runtime).

Maybe it could be. If you wish to change pow_fast to this, open a PR. There can be a debate as to which one is better. However, as separate package there is no debate: use this one if the less accuracy and more speed is appropriate for your needs. Arguably, FastMath should be split from Base.

Also, one major purpose of this package is to allow for the bithacking to be held in a place that allows for extensions which enable compatbility with automatic differentiation. Extensions for:

• Enzyme
• ForwardDiff
• Tracker
• ReverseDiff
• MonteCarloMeasurements
• Measurements

currently exist for this function. More can be added on-demand. This allows for pow_fast to be safe in most AD contexts, though in some cases improved extensions could be created which improve performance.