Benchmark: Setting Canvas Pixel Bis

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(800, 300);
$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() + ')'; $ctx.fillRect(px.x, px.y, 1, 1); }
1×1 Image Data
for (var i=500;i--;){ var px=$pxls[$i++ % 10000], d=$px.data; d[0] = px.r; d[1] = px.g; d[2] = px.b; d[3] = px.a; } $ctx.putImageData($px, 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 dive into the explanation.

**Benchmark Definition**

The provided JSON represents a JavaScript microbenchmark for testing the performance of canvas pixel manipulation in different ways. The benchmark defines three test cases:

1. **fillRect/concat**: This test case uses string concatenation to build the `rgba` color string.
2. **fillRect/join**: This test case uses the `join()` method to concatenate the RGB and alpha values into a single string.
3. **1×1 Image Data**: This test case directly modifies the image data array instead of using `fillStyle` or `fillRect`.

**Options Compared**

The benchmark compares the performance of three different approaches:

* Using string concatenation (`+`) to build the `rgba` color string.
* Using the `join()` method to concatenate the RGB and alpha values into a single string.
* Modifying the image data array directly.

**Pros and Cons of Each Approach**

1. **String Concatenation (`+`)**:
	* Pros: Simple and easy to implement, doesn't require additional libraries or data structures.
	* Cons: Can be slower due to the overhead of creating temporary strings and concatenating them.
2. **Join Method (`join()`)**:
	* Pros: More efficient than string concatenation, as it avoids creating temporary strings.
	* Cons: Requires the `join()` method, which might not be supported by older browsers or platforms.
3. **Modifying Image Data Array**:
	* Pros: Direct access to image data can be faster, as it eliminates the need for string manipulation or drawing functions.
	* Cons: May require additional setup and memory allocation.

**Library and Its Purpose**

In this benchmark, no libraries are explicitly mentioned. However, the use of `Canvas` and its associated APIs (e.g., `getContext()`, `fillStyle`, `fillRect`) implies that the test assumes a modern browser environment with support for HTML5 canvas elements.

**Special JS Features or Syntax**

This benchmark does not require any special JavaScript features or syntax beyond what is standard in modern browsers. However, it's worth noting that the use of `$` and `$px` variables might be considered non-standard, as they are typically used in JavaScript templating engines (e.g., Handlebars).

**Other Alternatives**

If you wanted to explore alternative approaches for this benchmark, you could consider:

* Using a library like `canvas-utils` or `image-optimizer` to optimize the image data access and manipulation.
* Experimenting with different data structures, such as using an array of objects instead of arrays of pixels.
* Investigating other drawing functions, such as `fillText()` or `stroke()`, which might offer better performance characteristics.

Keep in mind that these alternatives would likely require significant changes to the benchmark code and test setup.

Related benchmarks:

Setting Canvas Pixel Bis (version: 0)

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

Created: 5 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):