Benchmark: Array Indice vs Slice

Script Preparation code:

const count=1000
arr = []
for (let i=0; i<count; i++) {
	arr.push(Math.round(Math.random()*255))
	arr.push(Math.round(Math.random()*255))
	arr.push(Math.round(Math.random()*255))
}

Tests:

index
for (let i=0; i<arr.length; i+=3) { [arr[i], arr[i+1], arr[i+2]] }
slice
for (let i=0; i<arr.length; i+=3) { arr.slice(i,i+3) }

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
index
slice

Fastest: N/A

Slowest: N/A

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

LLMs can make mistakes. Check important info.

Let's break down the benchmark and explain what's being tested, compared, and their pros and cons.

**Benchmark Definition:**

The benchmark measures the performance difference between extracting RGB values from an `ImageData` object using either the `[a[5],a[6],a[7]]` syntax or the `arr.slice(5,8)` method.

**Options Compared:**

1. **[a[5],a[6],a[7]]**: This approach uses array indexing to access individual elements of the `ImageData` object's pixel data.
2. **arr.slice(5,8)**: This approach uses the `slice()` method to extract a subset of pixels from the `ImageData` object.

**Pros and Cons:**

1. **[a[5],a[6],a[7]]**:
	* Pros:
		+ Simple and straightforward syntax.
		+ Easy to read and understand for developers familiar with array indexing.
	* Cons:
		+ May not be as efficient due to the indirect access via `arr[a]`.
2. **arr.slice(5,8)**:
	* Pros:
		+ Efficient because it directly accesses the pixel data without unnecessary intermediate steps.
		+ Can take advantage of optimizations in the browser's engine.
	* Cons:
		+ May be less readable for developers unfamiliar with the `slice()` method.

**Library and Purpose:**

In this benchmark, no specific library is used beyond the built-in JavaScript features. However, it's worth noting that `ImageData` is a part of the HTML5 API, which relies on WebAssembly (WASM) under the hood. WASM provides optimized performance for certain tasks, including image processing.

**Special JS Feature or Syntax:**

There are no special JavaScript features or syntax used in this benchmark. The tests focus solely on comparing the efficiency of two specific approaches to extract pixel data from an `ImageData` object.

**Other Considerations:**

* **Cache Coherence:** Modern browsers have mechanisms like cache coherence to minimize memory access latency. In the context of this benchmark, it's essential to note that accessing pixels in a sequential manner using `arr.slice()` might benefit from such optimizations.
* **Browser Optimizations:** Each browser engine has its unique set of optimizations for specific tasks. The choice between `[a[5],a[6],a[7]]` and `arr.slice(5,8)` may be influenced by the browser's engine-specific optimizations.

**Alternatives:**

Other alternatives to compare could include:

1. **Canvas API:** Using the Canvas API with WebGL might provide more optimized performance for image processing tasks.
2. **WebGL or WebGPU:** Utilizing WebGL or WebGPU might offer even better performance, especially when working with 3D graphics and compute-intensive operations.
3. **Native Code (e.g., C++):** If possible, using native code (e.g., C++) can provide the most optimized performance for image processing tasks.

Keep in mind that these alternatives would require significant changes to the benchmark setup and testing framework.

Latest run results:

Run details: (Test run date: 3 years ago)

User agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:87.0) Gecko/20100101 Firefox/87.0

Browser/OS: Firefox 87 on Windows

View result in a separate tab

Test name	Executions per second
index	28978.5 Ops/sec
slice	12367.2 Ops/sec

Related benchmarks:

Array Indice vs Slice (version: 0)

is it faster to extract rgb from ImageData with [a[5],a[6],a[7]] or a.slice(5,8)

Comparing performance of: index vs slice

Created: 3 years ago by: Guest

Jump to the latest result

index

slice

Suite status: <idle, ready to run>

Experimental features:

Fastest: N/A

Slowest: N/A

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