Benchmark: me vs chatgpt - MeasureThat.net

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AخA
 
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Tests:

Me

const arr = [8, 9, 3, 9, 6, 5, 1, 7, 3, 9, 4, 1, 7, 2, 8, 3, 2, 1, 4, 7];

const small5 = [arr[0]];
const big5 = [arr[0]];

for (const n of arr.slice(1)) {
  if (n < small5[4] || small5.length < 5) {
    for (let i = 0; i < 5; ++i) {
      if (n < small5[i] || i === small5.length - 1) {
        small5.splice(i, 0, n);
        break;
      }
    }
  }

  if (n > big5[4] || big5.length < 5) {
    for (let i = 0; i < 5; ++i) {
      if (n > big5[i]) {
        big5.splice(i, 0, n);
        break;
      } else if (i === big5.length - 1) {
        big5.push(n);
        break;
      }
    }
  }
}

let total = 0;

for (let i = 0; i < 5; ++i) {
  total += small5[i] + big5[i];
}

​x
 
const arr = [8, 9, 3, 9, 6, 5, 1, 7, 3, 9, 4, 1, 7, 2, 8, 3, 2, 1, 4, 7];​const small5 = [arr[0]];const big5 = [arr[0]];​for (const n of arr.slice(1)) {  if (n < small5[4] || small5.length < 5) {    for (let i = 0; i < 5; ++i) {      if (n < small5[i] || i === small5.length - 1) {        small5.splice(i, 0, n);        break;      }    }  }​  if (n > big5[4] || big5.length < 5) {    for (let i = 0; i < 5; ++i) {      if (n > big5[i]) {        big5.splice(i, 0, n);        break;      } else if (i === big5.length - 1) {        big5.push(n);        break;      }    }  }}​let total = 0;​for (let i = 0; i < 5; ++i) {  total += small5[i] + big5[i];}

Chatgpt

const arr = [8, 9, 3, 9, 6, 5, 1, 7, 3, 9, 4, 1, 7, 2, 8, 3, 2, 1, 4, 7];
const small5 = [];
const big5 = [];

for (const n of arr) {
  // Maintain 5 smallest numbers
  if (small5.length < 5 || n < small5[4]) {
    small5.push(n);
    small5.sort((a, b) => a - b); // Ensure the smallest numbers are sorted
    if (small5.length > 5) small5.pop(); // Keep only the smallest 5
  }

  // Maintain 5 largest numbers
  if (big5.length < 5 || n > big5[4]) {
    big5.push(n);
    big5.sort((a, b) => b - a); // Ensure the largest numbers are sorted
    if (big5.length > 5) big5.pop(); // Keep only the largest 5
  }
}

// Calculate the total sum
const total = small5.reduce((sum, num) => sum + num, 0) +
              big5.reduce((sum, num) => sum + num, 0);

 
const arr = [8, 9, 3, 9, 6, 5, 1, 7, 3, 9, 4, 1, 7, 2, 8, 3, 2, 1, 4, 7];const small5 = [];const big5 = [];​for (const n of arr) {  // Maintain 5 smallest numbers  if (small5.length < 5 || n < small5[4]) {    small5.push(n);    small5.sort((a, b) => a - b); // Ensure the smallest numbers are sorted    if (small5.length > 5) small5.pop(); // Keep only the smallest 5  }​  // Maintain 5 largest numbers  if (big5.length < 5 || n > big5[4]) {    big5.push(n);    big5.sort((a, b) => b - a); // Ensure the largest numbers are sorted    if (big5.length > 5) big5.pop(); // Keep only the largest 5  }}​// Calculate the total sumconst total = small5.reduce((sum, num) => sum + num, 0) +              big5.reduce((sum, num) => sum + num, 0);

Rendered benchmark preparation results:

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Previous results

Experimental features:

Memory measurements supported only in Chrome.
For precise memory measurements Chrome must be launched with --enable-precise-memory-info flag.
More information: Monitoring JavaScript Memory

Test case name	Result
Me
Chatgpt

Fastest: N/A

Slowest: N/A

Latest run results:

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

User agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/131.0.0.0 Safari/537.36

Browser/OS: Chrome 131 on Windows

View result in a separate tab

Test name	Executions per second
Me	707017.1 Ops/sec
Chatgpt	221905.9 Ops/sec

Autogenerated LLM Summary (model gpt-4o-mini, generated 4 months ago):

In this benchmark, we are comparing two different JavaScript implementations aimed at calculating the sum of the five smallest and five largest numbers in an array of integers. The two implementations are labeled as "Me" and "Chatgpt".

Test Cases Overview

Test Case: "Me"
- Benchmark Definition: This implementation utilizes a single traversal of the input array, arr, which contains a set of predefined integers. It maintains two arrays (small5 and big5) for the smallest and largest five numbers, respectively.
- Logic:
  - It initializes small5 and big5 with the first element of arr.
  - For each number in arr, it checks if the number should be included in the five smallest (small5) or largest (big5) arrays and inserts it in sorted order using splice.
  - After processing all numbers, the total of the five smallest and five largest values is calculated by summing their elements.
Test Case: "Chatgpt"
- Benchmark Definition: This implementation also traverses the same array but follows a different approach for maintaining the small5 and big5.
- Logic:
  - It initializes small5 and big5 as empty arrays.
  - For each number in arr, it pushes the number if it should belong to either small5 or big5, then sorts the arrays and pops the last element if they exceed five elements.
  - The final total is calculated similarly by summing the elements of small5 and big5.

Pros and Cons

"Me" Implementation:

Pros:
- Efficiently maintains sorted order during insertion, reducing the need to sort the array after adding each element.
- Likely to be faster for larger arrays since the array remains reasonably small (only five elements).
Cons:
- The use of splice may introduce overhead, as it has a linear time complexity relative to the number of elements in the array when inserting.

"Chatgpt" Implementation:

Pros:
- Simpler and potentially easier to understand due to its usage of Array.sort(), which is a common operation in JavaScript.
- Relies on the array's built-in methods, making it less error-prone regarding boundaries for maintaining five elements.
Cons:
- Sorting an array may introduce significant overhead, especially as the arrays are being sorted with each insertion. The sorting algorithm typically has a time complexity of O(n log n), which may lead to lower performance.

Performance Results

The benchmark results show that the "Me" test case achieves an execution rate of approximately 1,478,177 operations per second, while the "Chatgpt" implementation has a performance of about 453,713 operations per second. This indicates that the approach taken in the "Me" implementation is substantially faster than the one used in "Chatgpt".

Other Considerations

Performance Implications: The choice of algorithm can lead to dramatically different performance characteristics, even for seemingly similar tasks. Understanding the implications of data structure choices and the time complexities of operations (like sorting and inserting) is crucial for performance-sensitive applications.
Array Length: In this benchmark, both implementations only deal with small fixed-size arrays (5 elements), but the varying execution speeds may have more pronounced effects with larger datasets.
Javascript Environment: Both implementations were run in a Chrome browser, highlighting how JavaScript's performance can vary between different environments and how optimizations can be made depending on the engine's capabilities.

Alternatives

There are several other approaches that could be considered for this problem:

Heap Data Structures: Using a min-heap for the smallest five and a max-heap for the largest five could yield improved performance characteristics, particularly when the input size is large.
Sort and Slice: An initial sort of the array followed by slicing can also work for small arrays but might not be the most efficient for larger datasets.
Using Libraries: Libraries like Lodash provide convenient functions to deal with collections, but the performance benefit should be weighed against the overhead of library use.

By understanding these different implementations and their trade-offs, developers can make better-informed decisions on how to approach similar problems in their own work.

LLMs can make mistakes. Check important info.

### Test Cases Overview

1. **Test Case: "Me"**
   - **Benchmark Definition**: This implementation utilizes a single traversal of the input array, `arr`, which contains a set of predefined integers. It maintains two arrays (`small5` and `big5`) for the smallest and largest five numbers, respectively.
   - **Logic**:
     - It initializes `small5` and `big5` with the first element of `arr`.
     - For each number in `arr`, it checks if the number should be included in the five smallest (`small5`) or largest (`big5`) arrays and inserts it in sorted order using `splice`.
     - After processing all numbers, the total of the five smallest and five largest values is calculated by summing their elements.

2. **Test Case: "Chatgpt"**
   - **Benchmark Definition**: This implementation also traverses the same array but follows a different approach for maintaining the `small5` and `big5`.
   - **Logic**:
     - It initializes `small5` and `big5` as empty arrays.
     - For each number in `arr`, it pushes the number if it should belong to either `small5` or `big5`, then sorts the arrays and pops the last element if they exceed five elements.
     - The final total is calculated similarly by summing the elements of `small5` and `big5`.

### Pros and Cons

**"Me" Implementation:**
- **Pros**:
  - Efficiently maintains sorted order during insertion, reducing the need to sort the array after adding each element.
  - Likely to be faster for larger arrays since the array remains reasonably small (only five elements).
- **Cons**:
  - The use of `splice` may introduce overhead, as it has a linear time complexity relative to the number of elements in the array when inserting.

**"Chatgpt" Implementation:**
- **Pros**:
  - Simpler and potentially easier to understand due to its usage of `Array.sort()`, which is a common operation in JavaScript.
  - Relies on the array's built-in methods, making it less error-prone regarding boundaries for maintaining five elements.
- **Cons**:
  - Sorting an array may introduce significant overhead, especially as the arrays are being sorted with each insertion. The sorting algorithm typically has a time complexity of O(n log n), which may lead to lower performance.

### Performance Results
The benchmark results show that the "Me" test case achieves an execution rate of approximately **1,478,177** operations per second, while the "Chatgpt" implementation has a performance of about **453,713** operations per second. This indicates that the approach taken in the "Me" implementation is substantially faster than the one used in "Chatgpt".

### Other Considerations
- **Performance Implications**: The choice of algorithm can lead to dramatically different performance characteristics, even for seemingly similar tasks. Understanding the implications of data structure choices and the time complexities of operations (like sorting and inserting) is crucial for performance-sensitive applications.
- **Array Length**: In this benchmark, both implementations only deal with small fixed-size arrays (5 elements), but the varying execution speeds may have more pronounced effects with larger datasets.
- **Javascript Environment**: Both implementations were run in a Chrome browser, highlighting how JavaScript's performance can vary between different environments and how optimizations can be made depending on the engine's capabilities.

### Alternatives
There are several other approaches that could be considered for this problem:
- **Heap Data Structures**: Using a min-heap for the smallest five and a max-heap for the largest five could yield improved performance characteristics, particularly when the input size is large.
- **Sort and Slice**: An initial sort of the array followed by slicing can also work for small arrays but might not be the most efficient for larger datasets.
- **Using Libraries**: Libraries like Lodash provide convenient functions to deal with collections, but the performance benefit should be weighed against the overhead of library use.