Benchmark: Canvas gradient perf

HTML Preparation code:

<canvas id="canvas1" width="512" height="512"></canvas>

<style>
 canvas { border: 1px solid black; }
</style>

<script>
var canvas = document.getElementById('canvas1');
var ctx = canvas.getContext('2d');

var grad = ctx.createLinearGradient(0, 0, 1, 64);
grad.addColorStop(0, 'red');
grad.addColorStop(0.5, 'rgba(0,0,255,0.5)');
grad.addColorStop(1, 'lime');

</script>

Tests:

Create
for (var i = 0; i < 10; i++) { var grad2 = ctx.createLinearGradient(0, 0, 1, 64); grad2.addColorStop(0, 'red'); grad2.addColorStop(0.5, 'rgba(0,0,255,0.5)'); grad2.addColorStop(1, 'lime'); ctx.fillStyle = grad2; ctx.fillRect(0,0,512,512); }
reuse
for (var i = 0; i < 10; i++) { ctx.fillStyle = grad; ctx.fillRect(0,0,512,512); }

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
Create
reuse

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 provided benchmark and explain what's being tested, compared, and their pros and cons.

**What is being tested?**

The benchmark tests two approaches to drawing a gradient on a canvas element:

1. **Create**: In this approach, a new linear gradient object (`grad2`) is created in each iteration of the loop.
2. **Reuse**: In this approach, the same linear gradient object (`grad`) is used across all iterations.

**Options being compared**

The two options being compared are:

* Creating a new gradient object for each iteration (`Create`)
* Reusing the same gradient object for all iterations (`Reuse`)

**Pros and Cons of each approach:**

1. **Create**: Pros:
	* Each pixel is drawn with the latest color stops.
	* Avoids potential issues with gradients being reused across iterations.
Cons:
	* Creates a new gradient object in each iteration, which can lead to performance overhead due to garbage collection and object creation.
2. **Reuse**: Pros:
	* Potential performance benefit from reusing the same object.
Cons:
	* May cause color interpolation issues if the gradient is changed between iterations.

**Library:**

The `createLinearGradient` method is a part of the 2D drawing context API in JavaScript, which creates a linear gradient object. This library provides a way to define a linear gradient with multiple color stops and apply it to a canvas element.

**Special JS feature or syntax:**

There's no special JS feature or syntax being used in this benchmark. However, note that the `var` keyword is used for variable declarations, which might be considered outdated by some modern JavaScript developers. It's worth mentioning that `const` and `let` are generally recommended over `var`.

**Benchmark preparation code:**

The script prepares a canvas element with an ID of "canvas1" and creates a 2D drawing context (`ctx`). A linear gradient object is created and added color stops to define the colors for each segment of the gradient.

**Other alternatives:**

If you wanted to test other approaches, you could consider:

* Using a different type of gradient (e.g., radial or conic)
* Applying the gradient only once and reusing it across iterations
* Using a library like fabric.js or paper.js to handle canvas manipulation

However, these alternatives would require significant changes to the benchmark definition and test cases.

It's worth noting that this benchmark seems to focus on understanding the performance implications of reusing an object versus creating new ones. If you're looking for more detailed optimization advice, consider using profiling tools like Chrome DevTools or Node.js Inspector to gain insight into your application's performance bottlenecks.

Related benchmarks:

Canvas gradient perf (version: 0)

Comparing performance of: Create vs reuse

Created: 8 years ago by: Guest

Jump to the latest result

Create

reuse

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):