Benchmark: Math.pow() vs **

Tests:

math.pow
var x = Math.pow(100, 4);
**
var x = 100 ** 4;

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
math.pow
**

Fastest: N/A

Slowest: N/A

Latest run results:

Run details: (Test run date: 5 years ago)

User agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/74.0.3729.169 Safari/537.36

Browser/OS: Chrome 74 on Windows

View result in a separate tab

Test name	Executions per second
math.pow	8438007.0 Ops/sec
**	1124955264.0 Ops/sec

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

LLMs can make mistakes. Check important info.

Let's break down the benchmark definition and test cases.

**Benchmark Definition JSON**

The provided JSON represents a benchmark definition, which outlines the general characteristics of the test case. Here's what we can extract from it:

* **Name**: "Math.pow() vs **" - This is the name of the benchmark, indicating that it compares the performance of `Math.pow()` with the exponentiation operator (`**`).
* **Description**: null - There is no description provided for this benchmark.
* **Script Preparation Code**: null - There is no script preparation code specified for this benchmark.
* **Html Preparation Code**: null - There is also no HTML preparation code provided.

This suggests that the test case is relatively simple, and the focus is on comparing the performance of two different ways to calculate exponentiation: using `Math.pow()` and the exponentiation operator (`**`).

**Individual Test Cases**

The JSON contains an array of individual test cases, each with its own benchmark definition. There are only two test cases:

1. **Test Case 1**: "var x = Math.pow(100, 4);" - This test case uses the `Math.pow()` function to calculate `x` as `100^4`.
2. **Test Case 2**: "var x = 100 ** 4;" - This test case uses the exponentiation operator (`**`) to calculate `x` as `100^4`.

**Library Usage**

In both test cases, no libraries are explicitly mentioned or used.

**Special JavaScript Features/Syntax**

There is one special feature/syntax used in these test cases:

* **Exponentiation Operator (`**`)**: This is a new syntax introduced in ECMAScript 2016 (ES6) as a shorthand for exponentiation. In older browsers, it might not be supported.
* `Math.pow()` function: This is an ancient function for calculating exponentiation, widely available in most JavaScript environments.

**Pros and Cons of Different Approaches**

Here's a brief analysis of the two approaches:

1. **`Math.pow()`**: This method has been around since the early days of JavaScript and is still supported by most browsers. However, it can be slower than the exponentiation operator (`**`) for large exponents.
2. **Exponentiation Operator (`**`)**: Introduced in ES6, this syntax is faster and more concise than using `Math.pow()`. However, older browsers may not support it.

**Other Considerations**

* The benchmark definition doesn't specify the number of iterations or the sampling interval, which might affect the accuracy of the results.
* There are no input parameters (like integers, floats, etc.) to test for performance variations across different types of data.

**Alternatives**

If you wanted to explore alternative approaches, here are a few options:

1. **Binary Exponentiation**: Instead of using `Math.pow()` or the exponentiation operator (`**`), you could use binary exponentiation algorithms (e.g., Karatsuba's algorithm) for fast exponentiation.
2. **Approximations**: For specific cases where exact calculation is not necessary, approximations like Newton's method or Taylor series expansions might be used to speed up calculations.

Feel free to ask if you'd like me to elaborate on any of these points!

Related benchmarks:

Math.pow() vs ** (version: 0)

Comparing performance of: math.pow vs **

Created: 5 years ago by: Registered User

Jump to the latest result

math.pow

**

Suite status: <idle, ready to run>

Experimental features:

Fastest: N/A

Slowest: N/A

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