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

Script Preparation code:

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

Tests:

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

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
Math.sqrt(x)
x**0.5

Fastest: N/A

Slowest: N/A

Autogenerated LLM Summary (model llama3.2:3b, generated 25 days ago):

LLMs can make mistakes. Check important info.

Let's break down the provided JSON and explain what's being tested, compared options, pros and cons, and other considerations.

**Benchmark Definition**

The benchmark is designed to compare two ways of calculating the square root of a number: `Math.sqrt(x)` (square root using the Math library) vs `x**0.5` (exponentiation with a power of 0.5).

**Script Preparation Code**

The script preparation code generates an array of 10,000 random numbers between 0 and the current iteration index (`x`). This creates a large dataset for the benchmark to measure performance.

**Html Preparation Code**

There is no HTML preparation code provided, which means that the test cases are run in a headless environment or don't require any specific HTML setup.

**Individual Test Cases**

There are two test cases:

1. `Math.sqrt(x)`: This test case measures the performance of using the `Math.sqrt()` function to calculate the square root of each number in the array.
2. `x**0.5`: This test case measures the performance of calculating the square root of each number by raising it to the power of 0.5.

**Library: Math**

The Math library is used for both test cases, specifically for the `Math.sqrt()` function. The purpose of this library is to provide mathematical functions and operations that can be used in JavaScript applications.

**Special JS Feature/Syntax: None**

There are no special JavaScript features or syntax used in this benchmark.

**Other Alternatives**

To measure the performance of these test cases, other alternatives could include:

* Using a different programming language (e.g., Python, C++)
* Utilizing parallel processing or multi-threading to execute multiple iterations concurrently
* Implementing a custom square root algorithm using bitwise operations or polynomial approximations
* Using a Just-In-Time (JIT) compiler to optimize the performance of the test cases

**Comparison of Options**

The two options being compared are:

1. `Math.sqrt(x)`: This method uses a mathematical library function that is widely supported and optimized for performance.
2. `x**0.5`: This method uses exponentiation with a power of 0.5, which can be calculated using a simple multiplication operation.

Pros and Cons:

* `Math.sqrt(x)`:
	+ Pros: Widely supported, well-optimized, and accurate.
	+ Cons: May have performance overhead due to library function call.
* `x**0.5`:
	+ Pros: Fast execution speed, as it only involves a simple multiplication operation.
	+ Cons: May not be as accurate or reliable as the Math library function.

In general, for most use cases, using the Math library's `Math.sqrt()` function is recommended due to its accuracy and wide support. However, if performance is critical and the application has specific requirements, implementing a custom square root algorithm or utilizing alternative methods may be necessary.

Latest run results:

Run details: (Test run date: 2 years ago)

User agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/104.0.0.0 Safari/537.36

Browser/OS: Chrome 104 on Windows

View result in a separate tab

Test name	Executions per second
Math.sqrt(x)	750.4 Ops/sec
x**0.5	26105.2 Ops/sec

Related benchmarks:

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

Comparing performance of: Math.sqrt(x) vs x**0.5

Created: 2 years ago by: Guest

Jump to the latest result

Math.sqrt(x)

x**0.5

Suite status: <idle, ready to run>

Experimental features:

Fastest: N/A

Slowest: N/A

Autogenerated LLM Summary (model llama3.2:3b, generated 25 days ago):