Benchmark: Finbonacci Performance test

Tests:

math
let phi = (1 + Math.sqrt(5))/2 function fib(n) { let asymp = Math.pow(phi, n) / Math.sqrt(5); return Math.round(asymp); } fib(50000)
for loop
let n = 50000 let arr = [1, 1]; for(let i = 0; i < n - 1; i++) { arr.push(arr[arr.length - 1] + arr[arr.length - 2]); }

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
math
for loop

Fastest: N/A

Slowest: N/A

Autogenerated LLM Summary (model llama3.2:3b, generated 27 days ago):

LLMs can make mistakes. Check important info.

I'd be happy to explain the benchmark and its components.

**Benchmark Definition**

The provided JSON represents a JavaScript microbenchmark, which is a small program designed to measure the performance of specific aspects of JavaScript execution. The benchmark definition includes:

1. **Name**: A unique identifier for the benchmark, "Finbonacci Performance test".
2. **Description**: A brief description of the benchmark, which seems to be gibberish in this case.
3. **Script Preparation Code** and **Html Preparation Code**: These fields are empty, indicating that no code is required to prepare the benchmark.

**Individual Test Cases**

The benchmark consists of two individual test cases:

1. **Test Case 1: "math"**
	* The **Benchmark Definition** includes a JavaScript function `fib(n)` that calculates the nth Fibonacci number using asymptotic approximation.
	* The **Test Name** is simply "math".
2. **Test Case 2: "for loop"**
	* The **Benchmark Definition** includes a simple loop that generates an array of Fibonacci numbers up to a specified length `n`.
	* The **Test Name** is again "for loop".

**Options Compared**

In this benchmark, two options are compared:

1. **Mathematical calculation (Fibonacci function)**: This option tests the performance of calculating Fibonacci numbers using asymptotic approximation.
2. **Loop-based calculation (Fibonacci array generation)**: This option tests the performance of generating an array of Fibonacci numbers using a loop.

**Pros and Cons**

The choice between these two options depends on the specific use case:

* For mathematical calculations, asymptotic approximation can be faster for large inputs like `n = 50000`.
* For loop-based calculations, the simplicity and predictability of the loop make it potentially faster for smaller inputs or when memory is limited.

However, in this benchmark, both options have similar performance characteristics, indicating that JavaScript's interpreter can handle these types of calculations efficiently.

**Library Use**

There is no explicit library mentioned in the benchmark definition. However, the Fibonacci function `fib(n)` uses a mathematical formula to calculate asymptotic approximation, which does not require any external libraries.

**Special JS Features or Syntax**

There are no specific JavaScript features or syntax mentioned in this benchmark.

**Other Alternatives**

If you wanted to create similar benchmarks, consider using tools like:

1. **Benchmark.js**: A popular tool for creating and running microbenchmarks.
2. **js-bench**: Another benchmarking library that provides a simple way to compare performance between different JavaScript implementations.
3. **Google's Benchmark**: An open-source benchmarking framework specifically designed for JavaScript.

These alternatives offer more features and options for customizing your benchmarks, such as support for multiple CPU cores, thread-based execution, and more.

Latest run results:

Run details: (Test run date: 2025 years ago)

User agent: Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/65.0.3325.162 Safari/537.36

Browser/OS: Chrome 65 on Linux

View result in a separate tab

Test name	Executions per second
math	1477338.0 Ops/sec
for loop	3097.1 Ops/sec

Related benchmarks:

Finbonacci Performance test (version: 0)

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Comparing performance of: math vs for loop

Created: 6 years ago by: Guest

Jump to the latest result

math

for loop

Suite status: <idle, ready to run>

Experimental features:

Fastest: N/A

Slowest: N/A

Autogenerated LLM Summary (model llama3.2:3b, generated 27 days ago):