The benchmark defined here is focused on comparing the performance of two different implementations of a 2D vector class, Vec2_int and Vec2_float, which represents mathematical vectors. The two classes differ primarily in how they define their properties.

Test Cases Overview

1. Vec2_int:

   • Properties: Both properties x and y are initialized to integers (0).
   • Methods:
     • randomize(): Assigns random float values to x and y.
     • length2(): Computes the squared length of the vector.
     • length(): Computes the actual length using the square root of the squared length.
   • Usage: In a loop, 10,000 instances of Vec2_int are created, random values assigned, and the lengths are calculated and averaged.

2. Vec2_float:

   • Properties: Both properties x and y are initialized to floats (0.5).
   • Methods: The methods are identical to those in Vec2_int.
   • Usage: Similar to Vec2_int, 10,000 instances are created, random values assigned, and lengths are calculated.

Comparison of Options

• Initialization with Integers vs. Floats:
  • Vec2_int starts with integer properties, whereas Vec2_float starts with float properties.
  • Pros of Using Floats: The float initialization (0.5) might provide an initial value closer to what would realistically be used in vector calculations (e.g., graphics, physics), where floating-point precision is crucial.
  • Cons of Using Integers: The integer initialization may lead to less realistic scenarios if the values will almost always be floating-point due to randomization.

Performance Results

• Executions per Second:
  • Vec2_float: 944.72 executions/second.
  • Vec2_int: 913.15 executions/second.
  • The Vec2_float implementation outperformed Vec2_int by a small margin.

Other Considerations

• Memory Usage: The memory usage might differ slightly for the two classes. Floats typically use more memory than integers (depends on the architecture), which could have a minor impact during garbage collection or memory access during the operation.
• Precision: Using floats is generally more suited for mathematical calculations, especially in scenarios where precision matters. Integers would lead to poorer performance in real-world vector applications, making Vec2_float a more suitable choice for practical applications.
• Garbage Collection: The JavaScript engine handles object allocation and garbage collection, but the number of instances created can have an effect on performance, especially in high-traffic applications.

Alternatives

• Typed Arrays: For numerical computations involving arrays of numbers, such as vectors, using JavaScript's Float32Array or Float64Array can be more efficient. These typed arrays offer a way to work with binary data and provide better performance for numerical calculations.
• Libraries: There are various libraries designed for vector math, such as GlMatrix or Three.js. These libraries provide optimized and well-tested implementations of vector mathematics that may outperform custom implementations.

In summary, while the benchmark is primarily about testing performance under similar mathematical operations, the choice of using integers vs. floats can lead to significant differences in practical applications, with the latter typically providing better performance and accuracy for vector operations.