Benchmark: Math pow and double asterix

Script Preparation code:

Math.pow(56, 2);

Tests:

56**2
56**2
Math.pow(56, 2);
Math.pow(56, 2);

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
56**2
Math.pow(56, 2);

Fastest: N/A

Slowest: N/A

Latest run results:

Run details: (Test run date: 15 days ago)

User agent: Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/142.0.0.0 Safari/537.36

Browser/OS: Chrome 142 on Linux

View result in a separate tab

Test name	Executions per second
56**2	211691216.0 Ops/sec
Math.pow(56, 2);	226314784.0 Ops/sec

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

LLMs can make mistakes. Check important info.

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

**Benchmark Definition JSON**

The provided JSON represents a single benchmark with the following properties:

* `Name`: The name of the benchmark, which is "Math pow and double asterix".
* `Description`: An empty string indicating no description for this benchmark.
* `Script Preparation Code`: A JavaScript statement that initializes a variable `x` to 56 using the exponentiation operator (**) instead of the traditional `Math.pow()` function. This code is executed before running the actual test.
* `Html Preparation Code`: An empty string, which suggests that no HTML-related setup or cleanup is required for this benchmark.

**Individual Test Cases**

The provided JSON also includes two individual test cases:

* The first test case uses a simple exponentiation operator (`56**2`) and has the same name as the test case.
* The second test case uses the `Math.pow()` function with arguments 56 and 2, and has the same name as the test case.

Now, let's analyze the options being compared:

1. **Exponentiation Operator (**) vs. `Math.pow()`**: Both operators are used to calculate the power of a number. However, there is a subtle difference in their implementation:
	* The exponentiation operator (`**`) uses a specialized CPU instruction (e.g., SSE/AVX instructions on x86 CPUs) that calculates powers much faster than the traditional `Math.pow()` function.
	* `Math.pow()`, on the other hand, uses a slower but more general-purpose algorithm to calculate powers.
	* Pros of using the exponentiation operator: Faster performance, especially for large exponents. Cons: Requires specialized CPU instructions and may not work correctly on all platforms or browsers.
	* Pros of using `Math.pow()` : More widely supported, easier to understand and implement, and works correctly on most platforms and browsers. Cons: Slower performance compared to the exponentiation operator.
2. **Inline Exponentiation (**) vs. `Math.pow()`**: In both cases, the operator is used to calculate powers. However, inline exponentiation (`56**2`) is optimized for performance by using a specialized CPU instruction, while `Math.pow(56, 2)` uses a slower but more general-purpose algorithm.

Considerations:

* When writing benchmarks, it's essential to consider the specific use case and requirements. If speed is critical, using inline exponentiation or other optimizations might be beneficial.
* However, if code readability and maintainability are more important than performance, using `Math.pow()` might be a better choice.
* Additionally, when running benchmarks across multiple platforms and browsers, it's crucial to ensure that the optimized code works correctly and consistently.

**Library and Special JS Features**

In this benchmark, no libraries or special JavaScript features are used. The test cases only rely on basic JavaScript syntax.

**Alternatives**

Other alternatives for calculating powers in JavaScript include:

* `**` with a multiplier (e.g., `56 * 56`)
* `Math.pow()` with arguments swapped (e.g., `Math.pow(2, 4)`)

Related benchmarks:

Math pow and double asterix (version: 0)

Comparing performance of: 56**2 vs Math.pow(56, 2);

Created: 6 years ago by: Guest

Jump to the latest result

56**2

Math.pow(56, 2);

Suite status: <idle, ready to run>

Fastest: N/A

Slowest: N/A

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