Benchmark: Setting Canvas Pixel 2

HTML Preparation code:

<canvas id="c" width="800" height="300"></canvas>

Script Preparation code:

$c = document.getElementById('c');
$ctx = $c.getContext('2d');
$ctx.clearRect(0, 0, 800, 300);
$px = $ctx.createImageData(1, 1);
$pxls = [];
for (var i=0; i<10000; ++i) $pxls.push({
   x: Math.random() * 800 << 0,
   y: Math.random() * 300 << 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:

fillRect/concat
for (var i=500;i--;){ var px = $pxls[$i++ % 10000]; $ctx.fillStyle = 'rgba(' + px.r + ',' + px.g + ',' + px.b + ',' + (px.a / 255) + ')'; $ctx.fillRect(px.x, px.y, 1, 1); }
fillRect/join
for (var i=500;i--;){ var px = $pxls[$i++ % 10000]; $ctx.fillStyle = 'rgba(' + [px.r,px.g,px.b,px.a/255].join() + ')'; $ctx.fillRect(px.x, px.y, 1, 1); }
1×1 Image Data
var imageData = $ctx.getImageData(0, 0, $c.width, $c.height); for (var i=500;i--;){ var px=$pxls[$i++ % 10000], d=$px.data; d[0] = px.r; d[1] = px.g; d[2] = px.b; d[3] = px.a; let k = (px.y) * $c.width + px.x; k = 4 * k; } $ctx.putImageData(imageData, 0,0);

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
fillRect/concat
fillRect/join
1×1 Image Data

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 benchmark and analyze the different approaches.

**Benchmark Overview**

The benchmark is designed to measure the performance of filling rectangles on a 2D canvas element using JavaScript. The script creates an image data object, populates it with random pixel values, and then uses the `fillRect` method to draw on the canvas. There are three test cases:

1. `fillRect/concat`: This test case uses string concatenation to construct the `fillStyle` string.
2. `fillRect/join`: This test case uses the `join()` method to concatenate the color values in an array.
3. `1×1 Image Data`: This test case uses the `getImageData` and `putImageData` methods to manipulate the image data directly.

**Options Compared**

The benchmark compares three different approaches:

* **String Concatenation**: Using string concatenation (`+`) to construct the `fillStyle` string.
* **Array Joining**: Using the `join()` method to concatenate the color values in an array.
* **Image Data Manipulation**: Using the `getImageData` and `putImageData` methods to manipulate the image data directly.

**Pros and Cons**

1. **String Concatenation**:
	* Pros: Simple, widely supported, and easy to implement.
	* Cons: Can lead to slower performance due to string creation and concatenation.
2. **Array Joining**:
	* Pros: More efficient than string concatenation since it avoids creating temporary strings.
	* Cons: May be less readable or more complex for some developers.
3. **Image Data Manipulation**:
	* Pros: Can potentially offer better performance by avoiding unnecessary string creation and concatenation.
	* Cons: Requires a deeper understanding of image data manipulation, which might not be familiar to all developers.

**Libraries Used**

None are explicitly mentioned in the benchmark definition or test cases. However, it's likely that the `Canvas` API and related functions (e.g., `createImageData`, `fillRect`) are part of the standard JavaScript library.

**Special JS Features/Syntax**

The benchmark uses a few advanced features:

* **Template literals** (in the script preparation code): Used to create a variable `$pxls` with an array of objects.
* **Bit-shifting operators** (`<< 0`): Used in some calculations, likely for performance optimization.

Overall, this benchmark helps identify performance differences between various approaches to filling rectangles on a canvas element, which can be useful for optimizing rendering code.

Related benchmarks:

Setting Canvas Pixel 2 (version: 0)

Comparing performance of: fillRect/concat vs fillRect/join vs 1×1 Image Data

Created: 6 years ago by: Guest

Jump to the latest result

fillRect/concat

fillRect/join

1×1 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):