Comparing the Efficiency of Pow() and exp() Functions

Has anyone else ever wondered whether exp() is quicker than the more general function pow()? I conducted a rapid benchmark on JsPerf, and the results were quite intriguing. You can view the test here.

Math.exp(logBase * exponent);  // performed the fastest
Math.exp(Math.log(base) * exponent);  // in the middle
Math.pow(base, exponent);  // the slowest

I understand that the outcomes can differ significantly depending on the system's architecture and programming language, but I am also curious about the theoretical aspect of it. Is pow(a, b) essentially implemented as exp(log(a) * b), or is there a more sophisticated method to compute power "directly" (in languages such as C++, C#, or JavaScript)? Are there specific CPU instructions dedicated to exp, log, or pow on certain devices?

To my knowledge, both exp() and log() are calculated using Taylor series and are resource-intensive processes. This leads me to believe that for a constant base of power, the following code snippet:

double logBase = log(123.456);
for (int i = 0; i < 1024; ++i) {
    exp(logBase * 654.321);
}

is more efficient than this one:

for (int i = 0; i < 1024; ++i) {
    pow(123.456, 654.321);
}

Is my assumption correct?

Answer №1

Absolutely, the efficiency of exp surpasses that of pow in most cases.

When it comes to optimizing for the target platform, exp and log functions have various techniques at their disposal like Pade approximation, linear or binary reduction followed by approximation, etc.

As mentioned, the implementation of the pow function often involves exp(log(a) * b), making it inherently slower compared to just using exp. This is especially true due to the added complexities of handling special cases with pow, such as negative exponents, integral exponents, exponents equal to 1/2 or 1/3, which can introduce further slowdowns.

For more insights, refer to this Stack Overflow discussion on pow.

Answer №2

When considering the intricate details of architecture, it is important to note that Math.pow requires more extensive error checking than Math.exp. For instance, what happens if the base is negative? This extra error handling in pow potentially makes it slower in comparison.

Key parts of the specifications include:

15.8.2.8 exp (x)

This function returns an implementation-specific approximation of the exponential function of x (e raised to the power of x, where e is the base of the natural logarithms).

If x is NaN, the result is NaN. If x is +0, the result is 1. If x is −0, the result is 1. If x is +∞, the result is +∞. If x is −∞, the result is +0.

15.8.2.13 pow (x, y)

This function also provides an implementation-dependent approximation to the result of raising x to the power y.

The conditions for different inputs are outlined in detail within the specification, covering various scenarios such as NaN values, positive or negative zeros, infinities, and other edge cases.

Answer №3

While some architectures do offer instructions for exp, log, or pow calculations, their usage may not always be practical.

For instance, on x86 architecture, there are operations like:

  • f2xm1 for calculating 2x - 1
  • fscale for computing y * 2(int)x
  • fyl2x for determining y * log2 x
  • fyl2xp1 for evaluating y * log2(x + 1) (with input range restrictions)

Despite the availability of these instructions, they are generally not widely used due to varying performance across different architectures. For example, fyl2x has a latency of 724 on Sandy Bridge processors, making manual implementations more efficient in many cases. The shift towards SSE code over FPU code is another factor diminishing the use of these instructions, as there are no SSE equivalents available.

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