Benchmark: (x ** 0.5) vs Math.sqrt(x)

Script Preparation code:

var numbers = Array.from(Array(10000), (_,x) => (Math.random()*x));

Tests:

Math.sqrt
numbers.forEach(x => Math.sqrt(x));
sqrt with ** 0.5
numbers.forEach(x =>x ** 0.5);

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
Math.sqrt
sqrt with ** 0.5

Fastest: N/A

Slowest: N/A

Latest run results:

Run details: (Test run date: one year ago)

User agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:136.0) Gecko/20100101 Firefox/136.0

Browser/OS: Firefox 136 on Windows

View result in a separate tab

Test name	Executions per second
Math.sqrt	7310.9 Ops/sec
sqrt with ** 0.5	14270.3 Ops/sec

Autogenerated LLM Summary (model llama3.2:3b, generated one year ago):

LLMs can make mistakes. Check important info.

I'll break down the benchmark and its components to explain what's being tested, compared, and their pros and cons.

**Benchmark Definition**

The provided JSON defines a benchmarking scenario with two test cases:

1. `(x ** 0.5) vs Math.sqrt(x)`
2. `numbers.forEach(x => Math.sqrt(x));`
3. `numbers.forEach(x => x ** 0.5);`

**What's being tested?**

These tests compare the performance of two approaches to calculate the square root of a number:

1. Using the exponentiation operator (`**`) with a fractional power (`0.5`).
2. Using the built-in `Math.sqrt()` function.

**Options compared**

The test cases compare these two approaches, which have different pros and cons:

* **Exponentiation operator (`**`) with a fractional power (`0.5`):**
	+ Pros:
		- Can be faster for certain use cases (e.g., when working with large numbers).
		- Often used in mathematical computations.
	+ Cons:
		- May not be as accurate or reliable as `Math.sqrt()` for all inputs (e.g., very small or very large numbers).
* **Built-in `Math.sqrt()` function:**
	+ Pros:
		- More accurate and reliable than the exponentiation operator approach.
		- Less prone to numerical instability issues.
	+ Cons:
		- May be slower due to the overhead of a built-in function.

**Library usage**

In both test cases, the `numbers` array is created using the `Array.from()` method with a callback function. This is not specific to any particular library; it's a standard JavaScript feature for creating arrays programmatically.

**Special JS features or syntax**

There are no special JavaScript features or syntaxes mentioned in this benchmarking scenario. It only uses standard JavaScript constructs and built-in functions (e.g., `Math.sqrt()`).

**Other alternatives**

For alternative approaches to calculate the square root, one might consider using:

* **Binary search**: a method that can be used to approximate the square root of a number.
* **Newton's method**: an iterative algorithm for approximating the square root of a number.

However, these alternatives are not explicitly mentioned in this benchmarking scenario and may have different performance characteristics compared to the built-in `Math.sqrt()` function or the exponentiation operator approach.

**Benchmark preparation code**

The script preparation code creates an array of 10,000 random numbers between 0 and 10000 using the `Array.from()` method with a callback function:
```javascript
var numbers = Array.from(Array(10000), (_,x) => (Math.random()*x));
```
This is not specific to any particular library; it's a standard JavaScript feature for creating arrays programmatically.

**Individual test cases**

The two test cases compare the performance of different approaches:

1. `numbers.forEach(x => Math.sqrt(x));`: uses the built-in `Math.sqrt()` function.
2. `numbers.forEach(x => x ** 0.5);`: uses the exponentiation operator (`**`) with a fractional power (`0.5`).

These test cases are likely used to determine which approach is faster and more efficient for calculating square roots in JavaScript.

Related benchmarks:

(x ** 0.5) vs Math.sqrt(x) (version: 0)

Comparing performance of: Math.sqrt vs sqrt with ** 0.5

Created: 2 years ago by: Guest

Jump to the latest result

Math.sqrt

sqrt with ** 0.5

Suite status: <idle, ready to run>

Fastest: N/A

Slowest: N/A

Autogenerated LLM Summary (model llama3.2:3b, generated one year ago):