Overview of the Benchmark

The provided benchmark measures the performance of three different approaches for matrix multiplication in JavaScript, specifically targeting web browsers.

Benchmark Definition JSON

The benchmark definition is an object that contains information about the test case:

• Name: The name of the test case ("matrix test")
• Description: A brief description of the test case
• Script Preparation Code: The JavaScript code required to prepare for the test case. This includes two custom functions, addObj and addArr, which perform matrix multiplication on 1D arrays (Float32Array) or objects.
• Html Preparation Code: An empty string, indicating that no HTML preparation is needed.

Individual Test Cases

There are three test cases:

1. vec3 Arr: This test case uses the addArr function to multiply two 2D matrices represented as 1D arrays (Float32Array) and stores the result in a third 1D array (Float32Array). The matrices have dimensions (100, 100) and are multiplied by performing nested loops.
2. vec3 obj: This test case uses the addObj function to multiply two 2D objects, where each element is an object with three float32 values representing a row in the matrix. The matrices have dimensions (100, 100) and are multiplied using a similar approach to vec3 Arr.
3. vec3 arr2: This test case uses the addArr2 function, which is similar to addArr, but allows for optional padding of rows and columns. The matrices have the same dimensions as in vec3 Arr.

Library Used

None of the benchmark definitions explicitly use a library like NumJS or MathJS, although they do utilize native JavaScript arrays (Float32Array) and objects. However, some libraries may provide similar functionality to these custom functions.

Special JS Features/Syntax

The benchmark uses:

• Function expressions (e.g., var A = function() { ... })
• Array literals (e.g., var B = new Float32Array(300))
• Loop constructs (e.g., for loops)

These features are standard in modern JavaScript and do not require special knowledge.

Performance Comparison

The benchmark compares the performance of three different approaches:

1. vec3 Arr: Uses the addArr function to multiply matrices represented as 1D arrays.
2. vec3 obj: Uses the addObj function to multiply matrices represented as objects with float32 values.
3. vec3 arr2: Uses the addArr2 function, which allows for optional padding of rows and columns.

The performance results are reported in executions per second (ExecutionsPerSecond).

Pros and Cons

Each approach has its pros and cons:

• vec3 Arr: Fast and lightweight, but may not be suitable for large matrices due to the overhead of 1D array indexing.
  • Pros: Low memory usage, fast execution
  • Cons: May be slower for large matrices
• vec3 obj: More memory-efficient than vec3 Arr, as it uses objects instead of arrays.
  • Pros: Good memory efficiency, can handle large matrices
  • Cons: May have overhead due to object creation and indexing
• vec3 arr2: Provides optional padding, making it more flexible for different use cases.
  • Pros: Flexible padding, good performance
  • Cons: May have overhead due to conditional logic

Alternatives

Some alternative approaches could be:

• Using a dedicated library like NumJS or MathJS, which provides optimized matrix multiplication functions.
• Implementing a more advanced algorithm, such as Strassen's matrix multiplication algorithm.
• Using parallelization techniques to take advantage of multi-core processors.

However, the benchmark is focused on measuring JavaScript performance in web browsers, so these alternative approaches may not be directly applicable.