function pow(base, power) {
if (power === 0) {
return 1;
}
if (power % 2 === 0) {
const multiplier = pow(base, power / 2);
return multiplier * multiplier;
}
const multiplier = pow(base, Math.floor(power / 2));
return multiplier * multiplier * base;
}
var a = pow(2,20)
function pow2(x, y) {
var q = parseInt((y / 2), 10);
if (y % 2 !== 0) {
return (x ** q) * (x ** q) * x;
}
return (x ** q) * (x ** q);
}
var a = pow2(2,20)
--enable-precise-memory-info
flag.
Test case name | Result |
---|---|
yours | |
my |
Test name | Executions per second |
---|---|
yours | 115701.6 Ops/sec |
my | 268511.2 Ops/sec |
Let's break down the benchmark definition and test cases to understand what is being tested.
Benchmark Definition
The benchmark definition represents a JavaScript function that calculates the power of a given base number raised to a certain exponent using an iterative approach with exponentiation by squaring. The function takes two parameters: base
and power
.
There are two different implementations of this function:
pow(base, power)
:
This implementation uses a recursive approach with memoization to avoid redundant calculations.
pow2(x, y)
:
This implementation uses a slightly different approach, but it also has some similarities with the first one. It appears to be an attempt to optimize the previous implementation by reducing the number of multiplications required for even exponents.
Options being compared
The two implementations are being compared in terms of their performance. Specifically:
pow(base, power)
:pow2(x, y)
:Pros and cons
pow(base, power)
:pow2(x, y)
:Library
There is no specific library mentioned in the benchmark definition or test cases. However, it's worth noting that both implementations use built-in JavaScript functions (Math.floor
and parseInt
) without any additional libraries.
Special JS features or syntax
None of the provided code uses any special JavaScript features or syntax beyond what is standard for JavaScript programming.
Other alternatives
There are other possible ways to implement exponentiation by squaring, such as:
It's worth noting that for small exponents, the difference in performance between these implementations may be negligible. However, as the exponent increases, the more efficient implementation is likely to show significant advantages.
Keep in mind that measuring performance and optimizing code can be complex tasks, and there are many factors that influence the execution time of a given function. This analysis is based on a simple interpretation of the provided code and benchmark results.