Benchmark: Matrix vs Linear

Script Preparation code:

const a = 1000
const b = 1000

var arr = Array.from({
    length: a * b
}, (_, k) => k)

var matrix = Array.from({
    length: a
}, (_, i) => Array.from({
    length: b
}, (_, j) => i + "/" + j))

Tests:

Matrix
matrix.forEach((row)=>{ row.forEach((i)=>{ console.log(i) }) })
Linear
arr.forEach((row)=>{ console.log(row) })

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
Matrix
Linear

Fastest: N/A

Slowest: N/A

Latest run results:

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

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Let's break down what's being tested in this benchmark.

**Benchmark Overview**

The benchmark is designed to compare the performance of two approaches: matrix operations and linear array operations. Specifically, it tests the speed of iterating over a 2D array (matrix) versus a 1D array (linear).

**Script Preparation Code**

The script preparation code generates a large 2D array (`matrix`) with dimensions `a` x `b`, where `a` is set to 1000 and `b` is also set to 1000. The array is created using `Array.from()` and the `length` property.

**Html Preparation Code**

There is no HTML preparation code, which means that this benchmark is purely client-side JavaScript performance testing.

**Test Cases**

The benchmark consists of two individual test cases:

1. **Matrix**: This test case iterates over the rows of the 2D array (`matrix`) using `forEach()` and then over each row again using another `forEach()`. The inner loop logs the element values to the console.
2. **Linear**: This test case iterates over the elements of the 1D linear array (`arr`) using `forEach()` and logs each element value to the console.

**Library Used**

Both tests use the built-in `Array.prototype.forEach()` method, which is a standard JavaScript library function for iterating over arrays.

**Special JS Features/ Syntax**

There are no special JavaScript features or syntax used in this benchmark. It's purely vanilla JavaScript code.

**Comparison of Approaches**

The main difference between the two approaches is the dimensionality of the data structure being iterated over:

* **Matrix**: The 2D array has a more complex iteration pattern, as it requires iterating over rows and then again within each row.
* **Linear**: The 1D linear array has a simpler iteration pattern, as it only requires iterating once.

**Pros and Cons**

Pros of using matrix operations (e.g., `matrix.forEach()`):

* Can be beneficial for certain algorithms that require accessing elements in a structured way.
* May lead to more readable code due to the explicit row-column access.

Cons:

* Can be slower due to the additional iteration over rows.
* May lead to increased memory usage.

Pros of using linear array operations (e.g., `arr.forEach()`):

* Generally faster, as it requires less iteration overhead.
* Typically uses less memory.

Cons:

* Less readable code, as elements are accessed sequentially without explicit row-column access.
* May not be suitable for algorithms that require structured access to data.

**Other Alternatives**

If you're looking for alternative approaches or libraries for performance testing JavaScript code, consider the following options:

1. **Benchmarking libraries**: Benchmarking libraries like `benchmark.js`, `fast-benchmark`, or `js-bench` can help you write and run benchmarks more efficiently.
2. **Profiling tools**: Profiling tools like Chrome DevTools' Performance Tab or Firefox Developer Edition's Profiler can provide detailed insights into your code's performance bottlenecks.
3. **WebAssembly benchmarking libraries**: If you're targeting WebAssembly (WASM) performance, consider using libraries like `wasm-benchmark` or `wasmperf`.

Related benchmarks:

Matrix vs Linear (version: 9)

Comparing performance of: Matrix vs Linear

Created: 2 years ago by: Registered User

Jump to the latest result

Matrix

Linear

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):