Benchmark: Matrix multiplication - TypedArray vs Object Class

Script Preparation code:

class Matrix {
    a = 1;
    b = 0;
    c = 0
    d = 1;
    tx = 0
    ty = 0;
}

var matrixObject = new Matrix();
var matrixObject2 = new Matrix();
var matrixObjectOut = new Matrix();

var matrixArray = new Float32Array([1, 0, 0, 1, 0, 0]);
var matrixArray2 = new Float32Array([1, 0, 0, 1, 0, 0]);
var matrixArrayOut = new Float32Array([1, 0, 0, 1, 0, 0]);

function multiplyMatrixObject(m1, m2, out) {
    out.a = (m1.a * m2.a) + (m1.b * m2.c);
    out.b = (m1.a * m2.b) + (m1.b * m2.d);
    out.c = (m1.c * m2.a) + (m1.d * m2.c);
    out.d = (m1.c * m2.b) + (m1.d * m2.d);
    out.tx = (m1.tx * m2.a) + (m1.ty * m2.c) + m2.tx;
    out.ty = (m1.tx * m2.b) + (m1.ty * m2.d) + m2.ty;
}

function multiplyMatrixArray(m1, m2, out) {
    out[0] = (m1[0] * m2[0]) + (m1[1] * m2[2]);
    out[1] = (m1[0] * m2[1]) + (m1[1] * m2[3]);
    out[2] = (m1[2] * m2[0]) + (m1[3] * m2[2]);
    out[3] = (m1[2] * m2[1]) + (m1[3] * m2[3]);
    out[4] = (m1[4] * m2[0]) + (m1[5] * m2[2]) + m2[4];
    out[5] = (m1[4] * m2[1]) + (m1[5] * m2[3]) + m2[5];
}

Tests:

Matrix Object
multiplyMatrixObject(matrixObject,matrixObject2, matrixObjectOut);
Matrix Array
multiplyMatrixArray(matrixArray,matrixArray2, matrixArrayOut);

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
Matrix Object
Matrix Array

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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The provided JSON represents two benchmark test cases: "Matrix multiplication - TypedArray vs Object Class". The goal of these benchmarks is to compare the performance of matrix multiplication operations using TypedArrays (a low-level, typed array data structure) and objects.

**Benchmark Definition**

The benchmark definition is a script that prepares the necessary variables and functions for the test. In this case, we have two main functions:

1. `multiplyMatrixObject(m1, m2, out)`: This function multiplies two matrices represented as object instances (`m1`, `m2`, and `out`). The matrix multiplication operation is performed using the standard formula (e.g., `(a * b) + (c * d)`).
2. `multiplyMatrixArray(m1, m2, out)`: This function multiplies two matrices represented as arrays (`m1`, `m2`, and `out`). The matrix multiplication operation is also performed using the standard formula.

**Options Compared**

The main options being compared are:

* **TypedArrays vs Objects**: We're comparing the performance of matrix multiplication operations using TypedArrays (a low-level, typed array data structure) versus objects (a high-level, dynamic data structure).
	+ Pros of TypedArrays:
		- Improved memory efficiency
		- Reduced garbage collection overhead
		- Faster access times due to caching
	+ Cons of TypedArrays:
		- Less flexibility and expressiveness compared to objects
		- Requires more manual management of memory allocation
	+ Pros of Objects:
		- Higher level of abstraction and ease of use
		- More flexible and expressive data structure
		- Less need for manual memory management
	+ Cons of Objects:
		- Potential performance overhead due to dynamic typing and garbage collection
* **Matrix Object vs Matrix Array**: We're comparing the performance of matrix multiplication operations using object instances (`m1`, `m2`, and `out`) versus arrays (`m1`, `m2`, and `out`).
	+ Pros of Matrix Objects:
		- Encapsulates data and behavior in a single unit
		- Can provide additional benefits like encapsulation and abstraction
	+ Cons of Matrix Objects:
		- May introduce overhead due to the creation of objects
		- Can lead to slower performance due to object overhead
	+ Pros of Matrix Arrays:
		- More memory-efficient compared to objects
		- Faster access times due to caching
	+ Cons of Matrix Arrays:
		- Less flexible and expressive data structure compared to objects

**Library**

In this benchmark, the `Float32Array` class is used as a TypedArray. It's a low-level array class that provides efficient storage for floating-point numbers.

**Special JS Features/Syntax**

There are no special JavaScript features or syntax being tested in this benchmark. The focus is on comparing different approaches to matrix multiplication operations.

**Alternatives**

If you're interested in exploring alternative benchmarking tools, here are some options:

1. **Google Benchmark**: A high-performance benchmarking library for C++ and JavaScript.
2. **Benchmark.js**: A popular benchmarking library for Node.js that provides a simple way to create benchmarks.
3. **JSPerf**: A benchmarking tool specifically designed for JavaScript, providing a simple way to compare the performance of different code snippets.

By understanding the options being compared in this benchmark, you can better appreciate the trade-offs between using TypedArrays and objects for matrix multiplication operations, as well as the benefits and drawbacks of using object instances versus arrays.

Related benchmarks:

Matrix multiplication - TypedArray vs Object Class (version: 0)

Comparing performance of: Matrix Object vs Matrix Array

Created: 3 years ago by: Registered User

Jump to the latest result

Matrix Object

Matrix Array

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):