Benchmark: Fib Now - MeasureThat.net

Tests:

Fib Base
const fibonacci = (n) => { if (n <= 1) return 1; return fibonacci(n - 1) + fibonacci(n - 2); }; fibonacci(20);
Fib memoized 1
const memoize = function(func) { var stringifyJson = JSON.stringify, cache = {}; var cachedfun = function() { var hash = stringifyJson(arguments); return hash in cache ? cache[hash] : (cache[hash] = func.apply(this, arguments)); }; cachedfun.__cache = function() { cache.remove || (cache.remove = function() { var hash = stringifyJson(arguments); return delete cache[hash]; }); return cache; }.call(this); return cachedfun; }; const fibonacci = memoize((n) => { if (n <= 1) return 1; return fibonacci(n - 1) + fibonacci(n - 2); }); fibonacci(20);
Fib memoized 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 Base
Fib memoized 1
Fib memoized Tommy

Fastest: N/A

Slowest: N/A

Latest run results:

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

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I'll dive into explaining the benchmark.

**Benchmark Overview**

The benchmark tests the performance of two implementations of the Fibonacci sequence: a baseline implementation (`Fib Base`) and two memoized versions (`Fib memoized 1` and `Fib memoized Tommy`). The benchmark uses a JavaScript function to calculate the nth Fibonacci number, which is then executed multiple times in a loop.

**Options Compared**

The three options being compared are:

1. **Baseline Implementation (`Fib Base`)**: This implementation calculates each Fibonacci number from scratch, without any optimization or caching.
2. **Memoized Version 1 (`Fib memoized 1`)**: This implementation uses a technique called memoization to cache previously computed Fibonacci numbers. It stores the results in an object and reuses them instead of recalculating them.
3. **Memoized Version 2 (`Fib memoized Tommy`)**: This implementation is similar to Memoized Version 1, but with some minor differences in the caching logic.

**Pros and Cons**

Here's a brief summary of the pros and cons of each approach:

* **Baseline Implementation (`Fib Base`)**:
	+ Pros: Simple to implement, easy to understand.
	+ Cons: Inefficient, as it recalculates the same Fibonacci numbers multiple times.
* **Memoized Version 1 (`Fib memoized 1`)**:
	+ Pros: Improves performance by caching previously computed Fibonacci numbers.
	+ Cons: Requires additional memory to store the cache, and the caching logic is not optimized.
* **Memoized Version 2 (`Fib memoized Tommy`)**:
	+ Pros: Similar performance improvement as Memoized Version 1, but with some minor optimizations.
	+ Cons: Still requires additional memory for caching, and the caching logic is similar to Memoized Version 1.

**Library/Technique Used**

Neither of the memoized versions uses a separate library. Instead, they rely on a simple object to store the cached values. The `memoize` function in both implementations creates this object and manages the caching logic.

**Special JS Features/Syntax**

There are no special JavaScript features or syntax used in this benchmark. It's purely functional programming with loops.

**Other Alternatives**

If you were to implement a memoized Fibonacci sequence, some alternative approaches could include:

* Using a library like `lru-cache` to handle caching and storage.
* Implementing a more advanced caching strategy, such as using a Least Recently Used (LRU) cache.
* Using a specialized data structure, such as a trie or a hash table, to store the cached values.

These alternatives would provide additional optimization opportunities, but also introduce new complexity and overhead.

Related benchmarks:

Fib Now (version: 0)

Comparing performance of: Fib Base vs Fib memoized 1 vs Fib memoized Tommy

Created: 6 years ago by: Guest

Jump to the latest result

Fib Base

Fib memoized 1

Fib memoized Tommy

Suite status: <idle, ready to run>

Fastest: N/A

Slowest: N/A

No previous run results

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