Benchmark: Fibonacci vs Memo

Tests:

FIbonnaci Memo
function fibMemo(index, cache) { cache = cache || []; if (cache[index]) return cache[index]; else { if (index < 3) return 1; else { cache[index] = fibMemo(index - 1, cache) + fibMemo(index - 2, cache); } } return cache[index]; } fibMemo(1000);
Fibonacci
function fibonacci(pos) { var a = [0,1] if (pos == 1) { return 1 } var i = 1 while (i < pos) { a.push(a[i] + a[i -1]) i++ } return a.pop() } fibonacci(1000)

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
FIbonnaci Memo
Fibonacci

Fastest: N/A

Slowest: N/A

Latest run results:

Run details: (Test run date: 7 years ago)

User agent: Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/61.0.3163.100 Safari/537.36

Browser/OS: Chrome 61 on Linux

View result in a separate tab

Test name	Executions per second
FIbonnaci Memo	18473.7 Ops/sec
Fibonacci	146336.7 Ops/sec

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

LLMs can make mistakes. Check important info.

I'll break down the provided benchmark and explain what's being tested, compared, and considered.

**Benchmark Overview**

The benchmark measures the performance difference between two implementations of the Fibonacci sequence calculation: a traditional recursive approach (Fibonacci) and an iterative memoized approach (Fibonacci Memo).

**What's Being Tested**

1. **Fibonacci**: This implementation uses recursion to calculate the nth Fibonacci number.
2. **Fibonacci Memo**: This implementation uses an iterative approach with memoization to cache previously computed values, reducing redundant calculations.

**Options Compared**

The benchmark compares two approaches:

1. **Traditional Recursion (Fibonacci)**: A recursive function that calls itself to calculate the nth Fibonacci number.
2. **Iterative Memoization (Fibonacci Memo)**: An iterative approach with memoization, which caches previously computed values to avoid redundant calculations.

**Pros and Cons**

* **Traditional Recursion (Fibonacci)**:
	+ Pros:
		- Easy to implement and understand
		- Simple syntax
	+ Cons:
		- Can lead to stack overflow errors for large inputs due to recursive calls
		- Slower performance compared to iterative approaches
* **Iterative Memoization (Fibonacci Memo)**:
	+ Pros:
		- Efficient use of cache to reduce redundant calculations
		- Fast performance for large inputs
	+ Cons:
		- Requires more complex implementation and understanding of memoization techniques

**Library Used**

None mentioned in the benchmark definition.

**Special JS Feature or Syntax**

No special JavaScript features or syntax are used in this benchmark.

**Other Considerations**

* **Cache**: The Fibonacci Memo approach uses a cache to store previously computed values, which can be beneficial for large inputs. However, this also increases memory usage.
* **Loop Unrolling**: Neither implementation is using loop unrolling, which could potentially improve performance.

**Alternatives**

For benchmarking similar use cases:

1. **Naive Recursion**: Implementing a naive recursive approach without memoization would likely outperform both the Fibonacci and Fibonacci Memo approaches.
2. **Dynamic Programming**: A dynamic programming approach with bottom-up construction would likely be more efficient than the iterative memoized approach.
3. **Parallel Processing**: For large inputs, parallel processing using techniques like multi-threading or parallelization could significantly improve performance.

These alternatives are not mentioned in the benchmark definition, but they can provide additional insights into the relative performance of different approaches for calculating the Fibonacci sequence.

Related benchmarks:

Fibonacci vs Memo (version: 0)

Comparing performance of: FIbonnaci Memo vs Fibonacci

Created: 7 years ago by: Guest

Jump to the latest result

FIbonnaci Memo

Fibonacci

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):