Benchmark: MMP Reverse Array

Script Preparation code:

var a = [...Array(100000).keys()];
var r = [];

Tests:

slice().reverse()
r = a.slice().reverse();
toReversed()
r = a.toReversed();
swap destructure
let i = 0, j = a.length - 1; while (i < j) [r[i++], r[j--]] = [a[j], a[i]];

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
slice().reverse()
toReversed()
swap destructure

Fastest: N/A

Slowest: N/A

Autogenerated LLM Summary (model llama3.2:3b, generated 24 days ago):

LLMs can make mistakes. Check important info.

Let's dive into the explanation of what's being tested in this benchmark.

**Overview**

This benchmark tests different ways to reverse an array of numbers using JavaScript. The benchmark is designed to measure the performance of these reversal techniques across various browsers and devices.

**Benchmark Definition JSON**

The provided JSON defines a single benchmark:

* `Name`: "MMP Reverse Array"
* `Description`: null (no description provided)
* `Script Preparation Code`: This code creates an array of 100,000 numbers using the `Array` constructor and assigns it to variable `a`.
* `Html Preparation Code`: null (no HTML preparation code is required)

**Individual Test Cases**

There are three test cases:

1. **`slice().reverse()`**: This test case uses the built-in `slice()` method followed by `reverse()`. The idea behind this approach is that `slice()` returns a new array with a reference to the original array, and then `reverse()` reverses the elements of the new array.
2. **`toReversed()`**: This test case uses an undefined function `toReversed()`, which is not a standard JavaScript method. It's likely that this function is specific to a library or framework being tested (more on this later).
3. **Swap destructure**: This test case uses a custom loop-based approach, where two pointers are swapped in place using destructuring assignment.

**Pros and Cons**

Here's a brief analysis of each approach:

* **`slice().reverse()`**: Pros: concise and easy to implement. Cons: creates a new array, which might lead to performance issues if the original array is large.
* **`toReversed()`**: Pros: none known (likely specific to a library or framework). Cons: undefined function, potentially slower due to unknown implementation.
* **Swap destructure**: Pros: no additional data structures are created. Cons: more complex and harder to understand.

**Other Considerations**

* The benchmark uses a large array of 100,000 numbers, which is likely chosen for its performance characteristics rather than practicality.
* The test cases do not account for edge cases like empty arrays or single-element arrays.

**Library/Frame Work Mentioned: `toReversed()`**

The function `toReversed()` is likely specific to a library or framework that provides an optimized reversal method. Without further information, it's difficult to determine the specifics of this implementation.

**Special JS Feature/Syntax**

There are no special JavaScript features or syntax mentioned in this benchmark.

**Alternatives**

Other ways to reverse an array include:

* Using `Array.prototype.reverse()` directly
* Using a custom algorithm like the "loop-destruction" approach (similar to swap destructure)
* Using a third-party library like Lodash, which provides a `reverse` method

Keep in mind that these alternatives might not be as performance-optimized as the specific methods being tested here.

Latest run results:

Run details: (Test run date: 10 months ago)

User agent: Mozilla/5.0 (X11; Ubuntu; Linux x86_64; rv:109.0) Gecko/20100101 Firefox/115.0

Browser/OS: Firefox 115 on Ubuntu

View result in a separate tab

Test name	Executions per second
slice().reverse()	698.9 Ops/sec
toReversed()	437.4 Ops/sec
swap destructure	247.5 Ops/sec

Related benchmarks:

MMP Reverse Array (version: 0)

Comparing performance of: slice().reverse() vs toReversed() vs swap destructure

Created: one year ago by: Registered User

Jump to the latest result

slice().reverse()

toReversed()

swap destructure

Suite status: <idle, ready to run>

Experimental features:

Fastest: N/A

Slowest: N/A

Autogenerated LLM Summary (model llama3.2:3b, generated 24 days ago):