Benchmark: Memoized Factorial

Script Preparation code:

const memoize = fn => {
 	const cache = {}; 
  	return (...args) => {
     	argStr = JSON.stringify(args);
		cache[argStr] = cache[argStr] || fn(args);

		return cache[argStr]; 
    }
}

const factorial = memoize((n) => {
	if (n <= 1) return 1;
	
  	return n * factorial(n - 1);
})

Tests:

Factorial memoized
const memoize = fn => { const cache = {}; return (...args) => { argStr = JSON.stringify(args); cache[argStr] = cache[argStr] || fn(args); return cache[argStr]; } } const factorial = memoize((n) => { if (n <= 1) return 1; return n * factorial(n - 1); }); factorial(10);
Factorial plain
const factorial =(n) => { if (n <= 1) return 1; return n * factorial(n - 1); }; factorial(10)

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
Factorial memoized
Factorial plain

Fastest: N/A

Slowest: N/A

Latest run results:

Run details: (Test run date: 2 years ago)

User agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/124.0.0.0 Safari/537.36

Browser/OS: Chrome 124 on Windows

View result in a separate tab

Test name	Executions per second
Factorial memoized	74246.6 Ops/sec
Factorial plain	11584203.0 Ops/sec

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

LLMs can make mistakes. Check important info.

I'll break down the provided benchmark and explain what's being tested, compared options, pros/cons of each approach, and other considerations.

**Benchmark Definition**

The benchmark measures the performance difference between two approaches to calculating the factorial of a number:

1. **Memoized Factorial**: This approach uses a caching mechanism to store previously computed values of the factorial function. When the same input is provided again, the cached value is returned instead of recomputing it.
2. **Plain Factorial**: This approach does not use any caching or optimization techniques.

**Script Preparation Code**

The script preparation code defines two functions:

* `memoize`: A higher-order function that takes another function as an argument and returns a new function with memoization enabled.
* `factorial`: The factorial function being benchmarked, which is either memoized (in the first test case) or plain (in the second test case).

**Html Preparation Code**

The html preparation code is empty, indicating that no specific HTML setup is required for this benchmark.

**Individual Test Cases**

There are two individual test cases:

1. **Factorial memoized**: This test case uses the `memoize` function to enable memoization for the `factorial` function.
2. **Factorial plain**: This test case does not use any optimization techniques, resulting in a plain implementation of the factorial function.

**Comparison**

The benchmark compares the performance of these two approaches:

* **Memoized Factorial** (test case 1): Uses caching to store previously computed values, reducing redundant computations.
* **Plain Factorial** (test case 2): Does not use any optimization techniques, resulting in more computationally expensive code.

**Pros and Cons**

**Memoized Factorial:**

Pros:

* Reduces computational overhead by avoiding redundant calculations.
* Can improve performance for large input values or repeated calls with the same input.

Cons:

* Requires additional memory to store cached values.
* May introduce complexity due to the caching mechanism.

**Plain Factorial:**

Pros:

* Simple and easy to understand implementation.
* Does not require additional memory or complex logic.

Cons:

* Can be computationally expensive for large input values or repeated calls with the same input.

**Other Considerations**

* The benchmark does not account for other factors that may affect performance, such as garbage collection, memory allocation, or other concurrent processes.
* The `memoize` function uses a simple caching mechanism, which might not be suitable for all use cases (e.g., cache invalidation, cache size limits).
* The benchmark assumes that the input values are integers; if non-integer inputs are expected, additional considerations would be necessary.

**Alternatives**

Other approaches to implementing factorial calculations could include:

* Using a lookup table or precomputed values for small input values.
* Employing iterative or recursive algorithms with optimizations (e.g., memoization, dynamic programming).
* Utilizing specialized libraries or functions optimized for performance (e.g., `Math.factorial` in modern browsers).

These alternatives would require additional testing and evaluation to determine their suitability for specific use cases.

Related benchmarks:

Memoized Factorial (version: 0)

Comparing performance of: Factorial memoized vs Factorial plain

Created: 8 years ago by: Guest

Jump to the latest result

Factorial memoized

Factorial plain

Suite status: <idle, ready to run>

Fastest: N/A

Slowest: N/A

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