Benchmark: Math.pow vs ** vs * (test)

Tests:

pow
var a = Math.pow(6, 5); var b = Math.pow(6, 10); var c = Math.pow(6, 30); var d = a + b + c;
**
var a = 6 ** 5; var b = 6 ** 10; var c = 6 ** 30; var d = a + b + c;
*
var a = 6 * 6 * 6 * 6 * 6 * 6; var b = 6 * 6 * 6 * 6 * 6 * 6 * 6 * 6 * 6 * 6 * 6; var c = 6 * 6 * 6 * 6 * 6 * 6 * 6 * 6 * 6 * 6 * 6 * 6 * 6 * 6 * 6 * 6 * 6 * 6 * 6 * 6 * 6 * 6 * 6 * 6 * 6 * 6 * 6 * 6 * 6 * 6 * 6; var d = a + b + c;

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
pow
**
*

Fastest: N/A

Slowest: N/A

Latest run results:

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

User agent: Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/101.0.4951.64 Safari/537.36

Browser/OS: Chrome 101 on Linux

View result in a separate tab

Test name	Executions per second
pow	3247337.2 Ops/sec
**	1364267008.0 Ops/sec
*	1365483392.0 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 provided benchmarking setup and explain what's being tested.

**Benchmark Definition**
The JSON object defines a benchmark with three test cases:

1. `Math.pow vs ** vs * (test)`
2. A script preparation code (empty in this case)
3. An HTML preparation code (also empty)

This suggests that the benchmark is comparing the performance of three different exponentiation methods: `Math.pow`, the unary `**` operator, and the multiplication operator (`*`).

**Test Cases**

Each test case is defined as an individual "test" with a unique name:

1. **pow**: This test case uses the `Math.pow` function to calculate the value of `6` raised to various powers (5, 10, and 30). The results are then added together.
2. **** : This test case uses the unary `**` operator to calculate the same values as above.
3. *** : This test case uses repeated multiplication (`*`) to achieve the same result.

Let's discuss the pros and cons of each approach:

* **Math.pow**: This method is explicit, readable, and well-supported by most programming languages. However, it may incur additional overhead due to the function call and parameter handling.
* **Unary `**` operator**: This method is concise and easy to read, as it leverages the built-in exponentiation operator. However, its performance might be lower than `Math.pow`, especially for larger exponents.
* **Repeated multiplication (`*`)**: This method is simple and efficient but can become cumbersome to read and maintain for large exponents.

Other considerations:

* The use of repeated multiplication (`*`) may introduce significant overhead due to the number of operations required to calculate large values.
* The `Math.pow` function might be optimized in some browsers or JavaScript engines, which could affect its performance compared to the unary `**` operator or repeated multiplication.

**Library Usage**
There is no explicit library usage mentioned in the benchmark definition. However, some implementations of the exponentiation methods might rely on underlying libraries or frameworks.

**Special JS Feature/ Syntax**
The benchmark uses the unary `**` operator, which was introduced in ECMAScript 2016 (ES6). This feature allows for concise and expressive exponentiation expressions.

**Alternatives**
Other alternatives to these exponentiation methods include:

* Using `Math.exp` with the calculated logarithm and product of the base and exponent
* Employing bit manipulation techniques to calculate large exponents efficiently
* Utilizing specialized libraries or frameworks optimized for exponentiation performance

Keep in mind that the choice of exponentiation method depends on the specific use case, readability requirements, and performance constraints.

Related benchmarks:

Math.pow vs ** vs * (test) (version: 1)

Comparing performance of: pow vs ** vs *

Created: 2 years ago by: Registered User

Jump to the latest result

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):