Benchmark: fib differnt fn - MeasureThat.net

Tests:

fib for
function fibonacci(num) { var num1=0; var num2=1; var sum; var i=0; for (i = 0; i < num; i++) { sum=num1+num2; num1=num2; num2=sum; } return num2; } fibonacci(25)
fib recursive
function getFib(num) { if (num === 0) { return 0; } else if (num === 1) { return 1; } else { return getFib(num - 1) + getFib(num - 2); } } getFib(25)

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
fib for
fib recursive

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/96.0.4664.110 Safari/537.36

Browser/OS: Chrome 96 on Linux

View result in a separate tab

Test name	Executions per second
fib for	59178340.0 Ops/sec
fib recursive	536.2 Ops/sec

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

LLMs can make mistakes. Check important info.

**Overview of the Benchmark**

The provided JSON represents two JavaScript microbenchmarks, each measuring the performance of different approaches to calculate the nth Fibonacci number. The benchmarks are designed to test the speed and efficiency of these approaches.

**Benchmark Definitions**

There are two benchmark definitions:

1. **Fibonacci using a simple loop**: This approach uses a for loop to iterate from 0 to `num`, calculating the sum of consecutive numbers in each iteration.
```javascript
function fibonacci(num) {
    var num1 = 0;
    var num2 = 1;
    var sum;
    var i = 0;
    for (i = 0; i < num; i++) {
        sum = num1 + num2;
        num1 = num2;
        num2 = sum;
    }
    return num2;
}
```
2. **Fibonacci using recursion**: This approach uses a recursive function to calculate the nth Fibonacci number.
```javascript
function getFib(num) {
  if (num === 0) {
    return 0;
  } else if (num === 1) {
    return 1;
  } else {
    return getFib(num - 1) + getFib(num - 2);
  }
}
```
**Comparison of Approaches**

The two approaches have different pros and cons:

* **Simple loop approach**:
	+ Pros: More predictable performance, easier to understand, and maintain.
	+ Cons: Can be less efficient for large values of `num`, as it involves more calculations.
* **Recursive approach**:
	+ Pros: Can be more elegant and concise, but can lead to stack overflows for large values of `num`.
	+ Cons: Performance is harder to predict, as it depends on the JavaScript engine's optimization.

**Library Use**

There are no libraries used in these benchmarks. However, if you were to modify or extend these benchmarks to use a library, some options could be:

* **Math.js**: A mathematical library that provides efficient implementations of mathematical functions.
* **Lodash**: A utility library that provides a range of functional programming helpers.

**Special JS Features**

The benchmark uses the following special JavaScript features:

* `var` declarations: Used for variable declaration in both benchmarks.
* Function expressions: Used to define the Fibonacci functions.

However, there are no other special JavaScript features used in these benchmarks.

**Other Alternatives**

If you wanted to create alternative benchmarks or approaches, some options could be:

* **Dynamic programming**: Use a memoization technique to store and reuse previously calculated values.
* **Iterative approach using bit manipulation**: Use bitwise operations to calculate the Fibonacci numbers more efficiently.
* **Parallel processing**: Use multi-threading or parallel processing to calculate multiple Fibonacci numbers simultaneously.

Overall, these benchmarks provide a simple and straightforward way to compare the performance of different approaches to calculating the nth Fibonacci number.

Related benchmarks:

fib differnt fn (version: 0)

Comparing performance of: fib for vs fib recursive

Created: 2 years ago by: Registered User

Jump to the latest result

fib for

fib recursive

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):