Benchmark: Factorials-comp - MeasureThat.net

Script Preparation code:

function recfac(n){
  if(n<0)return;
  return !n||n*recfac(n-1);
}
function itefac(n){
  if(n<0)return;
  let res=1;
  while(n>1)(res*=n,n-=1);
  return res;
}
function arrfac(n){
  if(n<0)return;
  return Array(n).fill().map((e,i)=>i+1).reduce((a,b)=>a*b);
}
let mem=[];
function memfac(n){
  if(n<0)return;
  return n<2?1:mem[n]||(mem[n]=n*memfac(n-1));
}

Tests:

Recursive
for(let i=1;i<71;i++)recfac(i);
Iterative
for(let i=1;i<71;i++)itefac(i);
Array Reduction
for(let i=1;i<71;i++)arrfac(i);
Memoization
for(let i=1;i<71;i++)memfac(i);

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
Recursive
Iterative
Array Reduction
Memoization

Fastest: N/A

Slowest: N/A

Latest run results:

No previous run results

This benchmark does not have any results yet. Be the first one to run it!

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

LLMs can make mistakes. Check important info.

**Benchmark Overview**

MeasureThat.net is a platform that allows users to create and run JavaScript microbenchmarks. The provided JSON represents a benchmark definition for calculating factorials using four different approaches: recursive, iterative, array reduction, and memoization.

**Approaches Compared**

1. **Recursive**: This approach uses a recursive function to calculate the factorial of a given number. The function calls itself with decreasing values until it reaches 0 or 1.
2. **Iterative**: This approach uses a loop to calculate the factorial of a given number. It iterates from 1 to n, multiplying the result by each number in the range.
3. **Array Reduction**: This approach uses the `reduce()` method of an array to calculate the factorial of a given number. The array is filled with numbers from 1 to n, and then reduced to a single value by multiplying all elements together.
4. **Memoization**: This approach uses an object (`mem`) to store previously calculated factorials for memoized values. If a factorial has already been calculated, it returns the stored result instead of recalculating it.

**Pros and Cons**

* **Recursive**:
	+ Pros: Simple to implement, easy to understand.
	+ Cons: Can lead to stack overflows for large inputs due to recursive function calls.
* **Iterative**:
	+ Pros: More efficient than recursive approach, avoids potential stack overflow issues.
	+ Cons: May require more memory and processing power due to the loop.
* **Array Reduction**:
	+ Pros: Efficient use of array operations, easy to implement.
	+ Cons: Requires a decent amount of memory for the temporary array, and may not perform well for very large inputs.
* **Memoization**:
	+ Pros: Can be very efficient by avoiding repeated calculations, especially for memoized values.
	+ Cons: May require additional memory for the object storing memoized results.

**Library Used (if applicable)**

In this case, no libraries are used. However, if you were to use a library like `lodash`, you might use its `reduce()` method to implement the Array Reduction approach.

**Special JavaScript Features or Syntax**

None mentioned in this benchmark.

**Other Alternatives**

1. **Dynamic Programming**: Another approach to calculating factorials, which can be more efficient than recursive approaches by storing intermediate results in an array.
2. **Bit Manipulation**: A technique for calculating large numbers efficiently using bit manipulation, which can be used to calculate factorials without requiring a lot of memory.

**Benchmark Preparation Code**

The benchmark preparation code is provided as JavaScript functions (`recfac`, `itefac`, `arrfac`, and `memfac`) that implement the different approaches. The `memfac` function uses an object (`mem`) to store memoized results, which are used to improve performance for repeated calculations.

**Test Case Preparation Code**

The test case preparation code is provided as a loop that runs each benchmark definition multiple times (in this case, 71 iterations) and measures the execution time for each approach.

Related benchmarks:

Factorials-comp (version: 0)

Comparing performance of: Recursive vs Iterative vs Array Reduction vs Memoization

Created: 8 years ago by: Guest

Jump to the latest result

Recursive

Iterative

Array Reduction

Memoization

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):