Benchmark: Compare GCD - MeasureThat.net

Script Preparation code:

function gcd(a, b){
  var divisor = 2, 
      greatestDivisor = 1;

  if (a < 2 || b < 2)
     return 1;
  
  while(a >= divisor && b >= divisor){
   if(a %divisor == 0 && b% divisor ==0){
      greatestDivisor = divisor;      
    }
   divisor++;
  }
  return greatestDivisor;
}

function gcd1(a,b){
    var divisor = a < b ? a : b;  	
    
    if(a < 2 || b < 2){
        return 1;
    }
    
    while(divisor > 0){
        if(a % divisor === 0 && b % divisor === 0){
            return divisor;
        }
        divisor--;
    }
}

function gcd2(a, b){
    var x;
    
    while(b !== 0){
        x = a % b;
        a = b;
        b = x;
    }
    
    return a;
}

function gcd3(a, b){
    if(b === 0)
        return a;
    return gcd3(b, a % b);
}

function gcd4(a,b){
    if (b === 0) {
        return a;
    } else {
        var remainder = a%b;
        return gcd4(b, remainder);
    }
}

Tests:

Original
gcd(1012, 10580);
Improvement
gcd1(1012, 10580);
Recursion
gcd3(1012, 10580);
Reverse Engineered Recursion
gcd2(1012, 10580);
Matt Solution
gcd4(1012, 10580);

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
Original
Improvement
Recursion
Reverse Engineered Recursion
Matt Solution

Fastest: N/A

Slowest: N/A

Autogenerated LLM Summary (model llama3.2:3b, generated 26 days ago):

LLMs can make mistakes. Check important info.

Let's break down the provided benchmark definitions and test cases.

**Benchmark Definition JSON**

The provided JSON represents a JavaScript microbenchmarking tool called MeasureThat.net. The `Script Preparation Code` section contains four different implementations of the Greatest Common Divisor (GCD) algorithm, which is used to calculate the largest number that divides two input numbers without leaving a remainder. The `Html Preparation Code` section is empty in this case.

The GCD algorithm is a fundamental problem in computer science and mathematics, and its implementation can vary significantly depending on the approach chosen. Let's examine each implementation:

1. **Original**: This is the simplest implementation of the GCD algorithm, which uses a simple loop to divide both inputs by increasing values until one of them becomes zero.
2. **Improvement**: This implementation appears to be an optimization over the original version, likely using some internal logic to reduce the number of iterations or divisions required.
3. **Recursion**: This implementation uses a recursive approach to calculate the GCD, which can lead to function calls and stack overflows for large inputs.
4. **Matt Solution** (or "gcd4" in the code): This implementation appears to be another optimization, possibly using a more efficient algorithm or a clever mathematical trick.

**Individual Test Cases**

The `Test Name` section lists four different test cases, each with its own GCD benchmark definition:

1. **Original**: This test case uses the original GCD implementation.
2. **Improvement**: This test case uses the improved GCD implementation.
3. **Recursion**: This test case uses the recursive GCD implementation.
4. **Matt Solution** (or "gcd4"): This test case uses Matt's optimized GCD algorithm.

Let's examine each test case:

1. **Original**: This test case is likely used to measure the baseline performance of the original GCD implementation.
2. **Improvement**: This test case is designed to evaluate the effectiveness of the improved GCD implementation, which may have reduced execution times or improved performance.
3. **Recursion**: This test case is intended to assess the performance of recursive algorithms and detect potential issues such as stack overflows or slow performance due to repeated function calls.
4. **Matt Solution** (or "gcd4"): This test case aims to measure the performance of Matt's optimized GCD algorithm, which may have reduced execution times or improved performance compared to other implementations.

**Library Usage**

There are no explicit libraries mentioned in the provided benchmark definition or test cases. However, it is possible that some custom implementation-specific functions or helper functions may be used within each test case.

**Special JS Features/Syntax**

None of the test cases appear to utilize any special JavaScript features or syntax beyond standard JavaScript implementations.

**Other Alternatives**

If you're interested in exploring alternative GCD implementations or optimization strategies, here are a few examples:

1. **Euclidean Algorithm**: This is another popular algorithm for calculating GCDs, which uses repeated divisions and remainders to find the common divisor.
2. **Binary GCD Algorithm**: This implementation uses bitwise operations to calculate GCDs efficiently.
3. **Fast GCD**: This optimization technique reduces the number of iterations required by using a more efficient division method or exploiting properties of the input numbers.

Keep in mind that these alternatives may not be directly applicable to the MeasureThat.net benchmarking tool, but they demonstrate other approaches to implementing and optimizing the GCD algorithm.

Feel free to ask if you'd like further clarification on any aspect of this analysis!

Latest run results:

Run details: (Test run date: 3 years ago)

User agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:87.0) Gecko/20100101 Firefox/87.0

Browser/OS: Firefox 87 on Windows

View result in a separate tab

Test name	Executions per second
Original	365883.0 Ops/sec
Improvement	372226.5 Ops/sec
Recursion	22119658.0 Ops/sec
Reverse Engineered Recursion	88874640.0 Ops/sec
Matt Solution	22868602.0 Ops/sec

Related benchmarks:

Compare GCD (version: 0)

Compare GCD

Comparing performance of: Original vs Improvement vs Recursion vs Reverse Engineered Recursion vs Matt Solution

Created: 8 years ago by: Guest

Jump to the latest result

Original

Improvement

Recursion

Reverse Engineered Recursion

Matt Solution

Suite status: <idle, ready to run>

Experimental features:

Fastest: N/A

Slowest: N/A

Autogenerated LLM Summary (model llama3.2:3b, generated 26 days ago):