Benchmark: Canvas Pixel Fill rect copmarison

HTML Preparation code:

<canvas id="c" width="1000" height="500"></canvas>

Script Preparation code:

$c = document.getElementById('c');
$ctx = $c.getContext('2d');
$ctx.clearRect(0, 0, 10000, 500);
$px = $ctx.createImageData(1000, 500);
$pxls = [];
for (var i=0; i<500000; ++i) $pxls.push({
   x: Math.random() * 1000 << 0,
   y: Math.random() * 500 << 0,
   r: Math.random() * 255 << 0,
   g: Math.random() * 255 << 0,
   b: Math.random() * 255 << 0,
   a: Math.random() * 128 << 0 + 128
});
$i = 0;

Tests:

1*1 fillRect/concat
for (var i=1000;i--;){ for(var j=500;j--;){ var px = $pxls[i*j] $ctx.fillStyle = 'rgba(' + px.r + ',' + px.g + ',' + px.b + ',' + (px.a / 255) + ')'; $ctx.fillRect(px.x, px.y, 1, 1); } }
Image Data
for (var i=0; i<500000; ++i){ var px=$pxls[i] ; $px.data[i][0] = px.r; $px.data[i][1] = px.g; $px.data[i][2] = px.b; $px.data[i][3] = px.a; } $ctx.putImageData($px, 1000,500);

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
1*1 fillRect/concat
Image Data

Fastest: N/A

Slowest: N/A

Latest 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 dive into the provided JavaScript benchmark.

**What is tested?**

The benchmark tests two approaches to filling rectangles on a 2D canvas context:

1. **Image Data**: This approach creates an image data object (`$px`) and fills its pixels with color data (`r`, `g`, `b`, and `a` values). The image data is then used to fill rectangles using the `putImageData()` method.
2. **1*1 fillRect/concat**: This approach uses a loop to iterate over the 500,000 pixel coordinates stored in `$pxls`. For each coordinate, it sets the fill style and fills a single rectangle with a size of 1x1.

**Options compared**

The two approaches differ in how they access and manipulate the canvas data:

* **Image Data**: This approach uses an image data object to store and manipulate pixel values. It's likely more efficient because the browser can use optimized graphics hardware to handle the image data.
* **1*1 fillRect/concat**: This approach uses a loop to iterate over each pixel coordinate, setting the fill style and filling rectangles individually. This approach may be less efficient due to the overhead of repeated calls to `fillStyle` and `fillRect`.

**Pros and Cons**

* **Image Data**:
	+ Pros: Likely more efficient due to optimized graphics hardware.
	+ Cons: May require additional setup (creating the image data object) and memory allocation.
* **1*1 fillRect/concat**:
	+ Pros: Simple and easy to implement.
	+ Cons: Less efficient due to repeated calls to `fillStyle` and `fillRect`.

**Library**

There is no explicit library mentioned in the benchmark definition. However, it's likely that the `$pxls` array and the canvas context are manipulated using native JavaScript APIs.

**Special JS feature or syntax**

None of the test cases use special JavaScript features or syntax beyond standard ECMAScript 5.

**Other considerations**

When designing benchmarks, consider the following:

* **Keep it simple**: Avoid overly complex benchmark scenarios that may lead to false positives.
* **Use meaningful test cases**: Ensure that the benchmark captures the performance characteristics of interest.
* **Consider multiple test runs**: Run each test case multiple times to account for variability and noise.

**Alternative approaches**

If you're interested in exploring alternative approaches, consider:

* Using a library like Web Workers or parallel processing frameworks (e.g., Node.js's `cluster` module) to leverage multi-core processors and improve performance.
* Employing caching techniques to reduce the number of times the image data object needs to be updated.
* Investigating hardware-accelerated graphics APIs, such as WebGL or Metal, for improved performance.

Keep in mind that these alternatives may require significant changes to your benchmark design and implementation.

Related benchmarks:

Canvas Pixel Fill rect copmarison (version: 0)

Comparing performance of: 1*1 fillRect/concat vs Image Data

Created: 5 years ago by: Guest

Jump to the latest result

1*1 fillRect/concat

Image Data

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):