Benchmark: quicksort for vs while

Tests:

for
let testArray = [1,3,1,2,31,23,5,6,8] function quickSort(arr){ if(arr.length <= 1) return arr const pivot = arr[arr.length - 1] let left = [] let right = [] for(let i = 0; i < arr.length-1; i++){ if (arr[i] < pivot) { left.push(arr[i]) } else { right.push(arr[i]) } } return [].concat(quickSort(left),pivot,quickSort(right)) } quickSort(testArray)
while
let testArray = [1,3,1,2,31,23,5,6,8] function quickSort(arr){ if(arr.length <= 1) return arr const pivot = arr.pop() let left = [] let right = [] while(arr[0]){ if (arr[0] < pivot) { left.push(arr.shift()) } else { right.push(arr.shift()) } } return [].concat(quickSort(left),pivot,quickSort(right)) } quickSort(testArray)

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
for
while

Fastest: N/A

Slowest: N/A

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

LLMs can make mistakes. Check important info.

**Benchmark Overview**

The provided benchmark measures the performance of two different approaches to implementing the quicksort algorithm in JavaScript: using a for loop and using a while loop.

**What is being tested?**

In this benchmark, we're testing how quickly each implementation can execute the following code:

```javascript
function quickSort(arr) {
  if (arr.length <= 1) return arr;
  const pivot = arr[arr.length - 1];
  let left = [];
  let right = [];
  for (let i = 0; i < arr.length - 1; i++) {
    if (arr[i] < pivot) {
      left.push(arr[i]);
    } else {
      right.push(arr[i]);
    }
  }
  return [].concat(quickSort(left), pivot, quickSort(right));
}
```

The test case uses an example array `testArray = [1,3,1,2,31,23,5,6,8]`.

**Options being compared**

Two options are being compared:

1. **For loop implementation**: This approach uses a traditional for loop to iterate through the array elements.
2. **While loop implementation**: This approach uses a while loop to iterate through the array elements.

**Pros and Cons of each approach**

**For Loop Implementation:**

Pros:

* Easy to understand and implement
* Can be optimized using traditional loop optimization techniques (e.g., caching, loop unrolling)

Cons:

* May have slower performance due to the overhead of the for loop syntax

**While Loop Implementation:**

Pros:

* Can lead to more efficient execution, as the while loop is more flexible and can avoid unnecessary iterations
* Can be optimized using more advanced techniques, such as loop unrolling and caching

Cons:

* May require more complex and harder-to-understand code
* Requires careful consideration of edge cases and array indexing

**Library usage**

There is no explicit library usage in this benchmark. However, the quicksort algorithm itself relies on JavaScript's built-in support for array manipulation.

**Special JS feature/syntax**

There are no special JavaScript features or syntax used in this benchmark. The implementation only uses standard JavaScript language constructs.

**Other alternatives**

To optimize the performance of the quicksort algorithm, other approaches could be explored, such as:

* Using a different sorting algorithm (e.g., mergesort, heapsort)
* Utilizing hardware acceleration (e.g., SIMD instructions, GPU acceleration)
* Optimizing loop unrolling and caching
* Using a hybrid approach that combines multiple algorithms

Keep in mind that the choice of optimization strategy depends on the specific requirements and constraints of the application.

Latest run results:

Run details: (Test run date: 6 years ago)

User agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/67.0.3396.87 Safari/537.36

Browser/OS: Chrome 67 on Windows

View result in a separate tab

Test name	Executions per second
for	355155.0 Ops/sec
while	323277.8 Ops/sec

Related benchmarks:

quicksort for vs while (version: 0)

Comparing performance of: for vs while

Created: 7 years ago by: Guest

Jump to the latest result

for

while

Suite status: <idle, ready to run>

Experimental features:

Fastest: N/A

Slowest: N/A

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