Here’s the guaranteed fastest possible sine function in C++:
double FastSin(double x)
{
return 0;
}
Oh, you wanted better accuracy than |1.0|? Well, here is a sine function that is similarly fast:
double FastSin(double x)
{
return x;
}
This answer actually does not suck, when x is close to zero. For small x, sin(x) is approximately equal to x, because x is the first term of the Taylor expansion of sin(x).
What, still not accurate enough for you? Well read on.
Engineers in the 1970s made some fantastic discoveries in this field, but new programmers are simply unaware that these methods exist, because they’re not taught as part of standard computer science curricula.
You need to start by understanding that there is no “perfect” implementation of these functions for all applications. Therefore, superficial answers to questions like “which one is fastest” are guaranteed to be wrong.
Most people who ask this question don’t understand the importance of the tradeoffs between performance and accuracy. In particular, you are going to have to make some choices regarding the accuracy of the calculations before you do anything else. How much error can you tolerate in the result? 10^-4? 10^-16?
Unless you can quantify the error in any method, don’t use it. See all those random answers below mine, that post a bunch of random uncommented source code, without clearly documenting the algorithm used and its exact maximum error across the input range? “The error is approximately sort of mumble mumble I would guess.” That’s strictly bush league. If you don’t know how to calculate the PRECISE maximum error, to FULL precision, in your approximation function, across the ENTIRE range of the inputs… then you don’t know how to write an approximation function!
No one uses the Taylor series alone to approximate transcendentals in software. Except for certain highly specific cases, Taylor series generally approach the target slowly across common input ranges.
The algorithms that your grandparents used to calculate transcendentals efficiently, are collectively referred to as CORDIC and were simple enough to be implemented in hardware. Here is a well documented CORDIC implementation in C. CORDIC implementations, usually, require a very small lookup table, but most implementations don’t even require that a hardware multiplier be available. Most CORDIC implementations let you trade off performance for accuracy, including the one I linked.
There’s been a lot of incremental improvements to the original CORDIC algorithms over the years. For example, last year some researchers in Japan published an article on an improved CORDIC with better rotation angles, which reduces the operations required.
If you have hardware multipliers sitting around (and you almost certainly do), or if you can’t afford a lookup table like CORDIC requires, you can always use a Chebyshev polynomial to do the same thing. Chebyshev polynomials require multiplies, but this is rarely a problem on modern hardware. We like Chebyshev polynomials because they have highly predictable maximum errors for a given approximation. The maximum of the last term in a Chebyshev polynomial, across your input range, bounds the error in the result. And this error gets smaller as the number of terms gets larger. Here’s one example of a Chebyshev polynomial giving a sine approximation across a huge range, ignoring the natural symmetry of the sine function and just solving the approximation problem by throwing more coefficients at it. And here’s an example of estimating a sine function to within 5 ULPs. Don’t know what an ULP is? You should.
We also like Chebyshev polynomials because the error in the approximation is equally distributed across the range of outputs. If you’re writing audio plugins or doing digital signal processing, Chebyshev polynomials give you a cheap and predictable dithering effect “for free.”
If you want to find your own Chebyshev polynomial coefficients across a specific range, many math libraries call the process of finding those coefficients “Chebyshev fit” or something like that.
Square roots, then as now, are usually calculated with some variant of the Newton-Raphson algorithm, usually with a fixed number of iterations. Usually, when someone cranks out an “amazing new” algorithm for doing square roots, it’s merely Newton-Raphson in disguise.
Newton-Raphson, CORDIC, and Chebyshev polynomials let you trade off speed for accuracy, so the answer can be just as imprecise as you want.
Lastly, when you’ve finished all your fancy benchmarking and micro-optimization, make sure that your “fast” version is actually faster than the library version. Here is a typical library implementation of fsin() bounded on the domain from -pi/4 to pi/4. And it just ain’t that damned slow.
One last caution to you: you’re almost certainly using IEEE-754 math to perform your estimations, and anytime you’re performing IEEE-754 math with a bunch of multiplies, then some obscure engineering decisions made decades ago will come back to haunt you, in the form of roundoff errors. And those errors start small, but they get bigger, and Bigger, and BIGGER! At some point in your life, please read “What every computer scientist should know about floating point numbers” and have the appropriate amount of fear. Keep in mind that if you start writing your own transcendental functions, you’ll need to benchmark and measure the ACTUAL error due to floating-point roundoff, not just the maximum theoretical error. This is not a theoretical concern; “fast math” compilation settings have bit me in the butt, on more than one project.
tl:dr; go google “sine approximation” or “cosine approximation” or “square root approximation” or “approximation theory.”