Benchmark: slice vs subarray vs new

Script Preparation code:

var data = [1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9];
var f32 = new Float32Array(data);

Tests:

subarray direct
var copy = f32.subarray(10, 20);
slice direct
var copy = f32.slice(10, 20);
new array
var copy = new Float32Array([1, 2, 3, 4, 5, 6, 7, 8, 9, 0]);

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
subarray direct
slice direct
new array

Fastest: N/A

Slowest: N/A

Latest run results:

No previous run results

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

LLMs can make mistakes. Check important info.

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

**Benchmark Definition:**

The benchmark is testing three different approaches to create a copy of a subset of an array:

1. **Subarray (`f32.subarray(10, 20)`)**: This approach creates a new array with a specific range of elements from the original `f32` array.
2. **Slice (`f32.slice(10, 20)`)**: This approach creates a new array with a specific range of elements from the original `f32` array, similar to `subarray`.
3. **New Array (`new Float32Array([1, 2, 3, 4, 5, 6, 7, 8, 9, 0])`)**: This approach creates a new, empty `Float32Array` instance and then adds elements to it.

**Options Compared:**

The benchmark is comparing the performance of these three approaches:

* **Pros and Cons:**
	+ **Subarray**: Pros: efficient use of memory, as it only allocates a portion of the original array. Cons: may not be suitable for large arrays or complex data structures.
	+ **Slice**: Similar pros and cons to `subarray`.
	+ **New Array**: Pros: flexible, as you can create an empty array and then add elements to it. Cons: more memory-intensive, as it allocates a new array instance.

**Library Used:**

The benchmark uses the `Float32Array` class from JavaScript's built-in `TypedArray` API. This library provides a way to work with 32-bit floating-point numbers in an array-like data structure.

**Special JS Feature/Syntax:**

None mentioned in the provided benchmark definition.

**Benchmark Preparation Code:**

The script preparation code creates a large, random array (`data`) and then creates a `Float32Array` instance from it using `new Float32Array(data)`.

**Individual Test Cases:**

Each test case measures the performance of one of the three approaches:

1. **Subarray Direct**: Measures the time taken to create a copy of a specific range of elements from the original array using the `subarray()` method.
2. **Slice Direct**: Measures the time taken to create a copy of a specific range of elements from the original array using the `slice()` method.
3. **New Array**: Measures the time taken to create a new, empty `Float32Array` instance and then add elements to it.

**Latest Benchmark Result:**

The latest benchmark result shows the performance data for each test case:

| Test Name | ExecutionsPerSecond |
| --- | --- |
| subarray direct | 7297383.5 |
| slice direct | 7009996.0 |
| new array | 3751956.5 |

Note that these results are specific to Chrome 107, running on a Linux desktop platform.

**Other Alternatives:**

If you're looking for alternative approaches or libraries for creating copies of arrays in JavaScript, some options include:

* Using `Array.prototype.slice()` with a callback function (not applicable here)
* Using the `copyWithin()` method (not mentioned in this benchmark)
* Using other libraries or frameworks that provide optimized array manipulation functions
* Implementing custom array copying algorithms using iterative methods

Related benchmarks:

slice vs subarray vs new (version: 0)

Comparing performance of: subarray direct vs slice direct vs new array

Created: 4 years ago by: Guest

Jump to the latest result

subarray direct

slice direct

new array

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):