Benchmark: Float32Array access

Script Preparation code:

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

Tests:

subarray
const [x, y, z] = positions.subarray(0, 3);
array
const x = positions[0]; const y = positions[1]; const z = positions[2];
at
const x = positions.at(0); const y = positions.at(1); const z = positions.at(2);

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
subarray
array
at

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 dive into the world of JavaScript microbenchmarks.

**Benchmark Overview**

The provided JSON represents a benchmark test for accessing elements in a `Float32Array` object. A `Float32Array` is a typed array data structure that stores 32-bit floating-point numbers.

**Benchmark Definition**

The benchmark definition consists of three script preparation codes, each testing a different approach to access the elements of the `Float32Array`. The scripts are:

1. **subarray**: This code tests accessing specific elements using the `subarray()` method.
2. **array**: This code tests accessing specific elements by indexing into the array directly (e.g., `positions[0]`, `positions[1]`, etc.).
3. **at**: This code tests accessing specific elements using the `at()` method.

**Comparison of Approaches**

Let's analyze each approach:

*   **subarray**: This approach involves creating a new subarray from the original `Float32Array` and then accessing its elements. The pros of this approach are:
    *   It can be used to access multiple elements at once, which might be beneficial in certain scenarios.
    *   It allows for more flexibility in terms of element selection (e.g., slicing).
*   **array**: This approach involves indexing directly into the `Float32Array` using its numerical indices. The pros of this approach are:
    *   It is often faster than `subarray()` since it avoids creating a new array.
    *   It can be more memory-efficient for large arrays.
*   **at**: This approach involves using the `at()` method, which provides a way to access specific elements in an array-like object. The pros of this approach are:
    *   It is often faster than indexing directly into the array (e.g., `positions[0]`) since it avoids bounds checking.
    *   It can be more readable for complex accesses.

The cons of each approach include:

*   **subarray**: Creating a new subarray can be slower and less memory-efficient than indexing directly into the original array.
*   **array**: Indexing directly into the array requires knowledge of its indices, which might not always be known beforehand. This approach also avoids bounds checking for `at()`, which could lead to errors if the index is out of range.
*   **at**: While it provides a more readable way of accessing elements, it can still incur overhead due to bounds checking.

**Library**

In this benchmark, there is no explicit library mentioned. However, `Float32Array` is a part of the JavaScript Standard Library, so any compliant browser should support it natively.

**Special JS Feature/Syntax**

There are no special features or syntax used in this benchmark that would require additional explanation.

**Alternatives**

Some alternatives to these approaches could be:

*   **Buffer**: Instead of using `Float32Array`, you might consider using the Buffer API, which provides a more efficient and compact way of handling binary data.
*   **Typed Array views**: You could use typed array views (e.g., `Float32View`) as an alternative to arrays. These provide a way to access specific elements in an array-like object without creating a new view or subarray.

Here's some sample code demonstrating these alternatives:
```javascript
// Using Buffer
const buf = Buffer.alloc(10);
buf.writeFloatLE(1, 0);
buf.writeFloatLE(2, 4);
console.log(buf.readInt32LE(0));

// Using Typed Array views
const float32View = new Float32Array(new ArrayBuffer(10));
float32View[0] = 1;
float32View[4] = 2;
console.log(float32View[0]);
```

These alternatives offer different trade-offs in terms of performance, memory usage, and complexity.

Latest run results:

Run details: (Test run date: one year ago)

User agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/111.0.0.0 Safari/537.36 Edg/111.0.1661.62

Browser/OS: Chrome 111 on Windows

View result in a separate tab

Test name	Executions per second
subarray	11830800.0 Ops/sec
array	7238480.5 Ops/sec
at	3646552.2 Ops/sec

Related benchmarks:

Float32Array access (version: 0)

Float32Array access test

Comparing performance of: subarray vs array vs at

Created: one year ago by: Guest

Jump to the latest result

subarray

array

at

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):