Benchmark: eSetting Canvas Pixel

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=200;i--;){ var px = $pxls[$i++ % 10000]; $ctx.fillStyle = 'rgba(' + px.r + ',' + px.g + ',' + px.b + ',' + (px.a / 255) + ')'; $ctx.fillRect(px.x*2, px.y*2, 2, 2); }
1×1 Image Data
for (var i=200;i--;){ var px=$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); $ctx.putImageData($px, px.x, px.y+1); $ctx.putImageData($px, px.x+1, px.y); $ctx.putImageData($px, px.x+1, px.y+1); }

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
fillRect/concat
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.

Measuring JavaScript performance is crucial for optimizing code and ensuring smooth user experiences. In this case, we're examining the `eSetting Canvas Pixel` benchmark on MeasureThat.net.

**Benchmark Overview**

The benchmark measures the performance of two different approaches to rendering small images (1x1 pixels) on a canvas:

1. **fillRect/concat**: This approach uses the `fillRect` method to draw small rectangles, concatenating multiple calls to improve performance.
2. **1×1 Image Data**: This approach retrieves the image data from the canvas, modifies it, and then draws it using various methods (putImageData).

**Options Compared**

The benchmark compares the performance of these two approaches:

Pros and Cons:

*   **fillRect/concat**:
    *   Pros: Easy to implement, simple code.
    *   Cons: May not be optimized for modern hardware, could lead to aliasing issues if not implemented carefully.
*   **1×1 Image Data**: This approach is more complex but might provide better performance and image quality due to the use of advanced graphics features.

**Library Usage**

The benchmark uses the following libraries:

*   None explicitly mentioned in the provided code. However, JavaScript's built-in canvas API is used for rendering images.
*   There are no specific external libraries like WebGL or Web Workers used in this example.

**Special JS Features/Syntax**

None of the benchmark examples use special JavaScript features or syntax that are not generally supported by modern browsers. The code should run on a wide range of environments without any issues related to these features.

**Alternative Approaches**

Other approaches for rendering small images on a canvas might include:

*   Using WebGL (Web Graphics Library) for more efficient graphics processing.
*   Leveraging Web Workers for parallel computation, which can be useful when dealing with complex or computationally intensive tasks.
*   Employing hardware-accelerated graphics APIs like Metal on macOS or Direct3D 12 on Windows.

However, these alternatives might require more significant changes to the benchmark code and might not offer substantial performance gains over the existing approaches.

In conclusion, this benchmark provides a simple yet informative comparison of two common methods for rendering small images on a canvas. Understanding the pros and cons of each approach can help developers optimize their own JavaScript code for better performance and user experiences.

Related benchmarks:

eSetting Canvas Pixel (version: 0)

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

Created: 7 years ago by: Guest

Jump to the latest result

fillRect/concat

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):