Benchmark: Setting Canvas Pixel

HTML Preparation code:

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

Script Preparation code:

$c = document.getElementById('c');
$ctx = $c.getContext('2d');
$ctx.clearRect(0, 0, 800, 800);
$px = $ctx.createImageData(1, 1);
$pxls = [];
for (var i=0; i<10000; ++i) $pxls.push({
   x: Math.random() * 800 << 0,
   y: Math.random() * 800 << 0,
   r: 250 << 0,
   g: 0 << 0,
   b: 0,
   a: 1
});
$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, px.x, px.y); }

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:

Run details: (Test run date: 2025 years ago)

User agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/64.0.3282.119 Safari/537.36 OPR/51.0.2830.26

Browser/OS: Opera 51 on Windows

View result in a separate tab

Test name	Executions per second
fillRect/concat	1088.8 Ops/sec
fillRect/join	891.7 Ops/sec
1×1 Image Data	98.8 Ops/sec

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

LLMs can make mistakes. Check important info.

Let's dive into explaining the benchmark.

**What is tested:**

The provided JSON represents a JavaScript microbenchmark that tests the performance of different approaches to filling a canvas pixel with an image data object. The benchmark creates a canvas element, sets up a 2D drawing context, and then fills the pixel with an image data object using three different methods:

1. **Concise syntax**: This approach uses string concatenation to build the `rgba` color string.
2. **Join method**: This approach uses the `join()` method to concatenate the color components into a single string.
3. **Indexed array access**: This approach accesses the color components directly from the image data object.

**Options compared:**

The benchmark compares the performance of these three approaches, which provides insight into the optimization techniques used by different browsers and engines.

**Pros and Cons:**

* **Concise syntax**: Pros:
	+ Simple and concise code.
	+ Easy to understand and maintain.
Cons:
	+ May lead to slower performance due to string concatenation.
* **Join method**: Pros:
	+ More efficient than string concatenation for large color strings.
	+ Can be faster in some cases.
Cons:
	+ Requires more memory accesses compared to direct array access.
* **Indexed array access**: Pros:
	+ Direct access to the color components can be faster.
	+ May lead to better cache locality.
Cons:
	+ More complex code, which can make it harder to understand and maintain.

**Library usage:**

None of the benchmark scripts explicitly use any external libraries. However, some features might rely on browser-specific APIs or engine optimizations that are not explicitly mentioned in the provided JSON.

**Special JS feature or syntax:**

* The `<< 0` syntax is used to perform a left shift operation, which is equivalent to multiplying by 2^16 (65536). This is likely used for precision and data type optimization.
* The `$pxls[$i++ % 10000]` syntax uses the variable `$pxls`, which is an array containing image data objects. The `% 10000` part ensures that the index wraps around to the beginning of the array after reaching the end, allowing for a large number of iterations.

**Alternatives:**

To compare the performance of these approaches, one could create more benchmark scripts using different methods, such as:

* Using `rgba()` function instead of string concatenation or join method.
* Implementing custom color manipulation functions for better optimization.
* Testing different data types (e.g., floating-point numbers instead of integers) to observe any additional optimizations.

Keep in mind that the choice of approach will depend on the specific use case, performance requirements, and personal preference.

Related benchmarks:

Setting Canvas Pixel (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>

Experimental features:

Fastest: N/A

Slowest: N/A

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