Benchmark: Fib Now 4 - MeasureThat.net

HTML Preparation code:

<script src="https://www.jsdelivr.com/package/npm/memoizee"></script>

Tests:

Fib Iterative
function fibonacci(num){ var a = 1, b = 0, temp; while (num >= 0) { temp = a; a = a + b; b = temp; num--; } return b; } fibonacci(20);
Fib Tommy
const memoize = function(func) { const cache= {} return (...args) => { const n = args[0] if (n in cache) { return cache[n]; } else { const result = func(n) cache[n] = result return result } } }; const fibonacci = memoize((n) => { if (n <= 1) return 1; return fibonacci(n - 1) + fibonacci(n - 2); }); fibonacci(20);

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
Fib Iterative
Fib Tommy

Fastest: N/A

Slowest: N/A

Latest run results:

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

User agent: Mozilla/5.0 (Macintosh; Intel Mac OS X 10_14_5) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/78.0.3904.87 Safari/537.36

Browser/OS: Chrome 78 on Mac OS X 10.14.5

View result in a separate tab

Test name	Executions per second
Fib Iterative	79596240.0 Ops/sec
Fib Tommy	691704.4 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 benchmark and its test cases to understand what's being tested.

**Benchmark Overview**

The benchmark, titled "Fib Now 4," consists of two individual test cases: "Fib Iterative" and "Fib Tommy." The benchmark is designed to measure the performance of different approaches for calculating the Fibonacci sequence.

**Test Case 1: Fib Iterative**

This test case uses a simple iterative approach to calculate the Fibonacci sequence. The JavaScript code defines a function `fibonacci` that takes an integer `num` as input and returns the `num`-th Fibonacci number. The function uses a while loop to calculate the Fibonacci numbers iteratively.

**Test Case 2: Fib Tommy**

This test case uses memoization, a technique where previously calculated results are stored in a cache to avoid redundant calculations. The JavaScript code defines a function `fibonacci` that takes an integer `n` as input and returns the `n`-th Fibonacci number. This implementation uses the `memoizee` library, which provides a simple way to implement memoization.

**Memoizee Library**

The `memoizee` library is used in Test Case 2 to implement memoization for the Fibonacci sequence calculation. The library provides a function `memoize` that takes another function as input and returns a new function with memoized behavior. In this case, the `memoize` function is used to wrap the original Fibonacci function, caching its results for future calls.

**Pros and Cons of Different Approaches**

1. **Iterative Approach (Fib Iterative)**:
	* Pros: Simple to implement, no additional memory required.
	* Cons: May require more iterations due to the lack of caching.
2. **Memoization Approach (Fib Tommy)**:
	* Pros: Can significantly improve performance by avoiding redundant calculations, especially for large inputs.
	* Cons: Requires additional memory to store the cache, which may not be feasible for very large inputs.

**Special JS Feature/Syntax**

There are no special JavaScript features or syntax used in these test cases. The code is standard JavaScript, and the use of memoization is a common technique to improve performance.

**Other Alternatives**

Other approaches that could be used to calculate the Fibonacci sequence include:

1. **Recursive Approach**: A recursive function can be defined to calculate the Fibonacci numbers, but this approach may not perform well due to the repeated calls and overhead.
2. **Dynamic Programming**: A dynamic programming approach can be used to store intermediate results in an array or other data structure, reducing the number of redundant calculations.

**Benchmark Considerations**

When interpreting the benchmark results, consider the following:

1. **Execution Speed**: The execution speed is a key metric for this benchmark. The faster the code executes, the better its performance.
2. **Memory Usage**: Memoization may require more memory to store the cache, which can impact performance on devices with limited memory.

By considering these factors and approaches, you can gain insights into the relative performance of different methods for calculating the Fibonacci sequence.

Related benchmarks:

Fib Now 4 (version: 0)

Comparing performance of: Fib Iterative vs Fib Tommy

Created: 5 years ago by: Guest

Jump to the latest result

Fib Iterative

Fib Tommy

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):