Benchmark: Multiplying lists of vectors - SoA vs AoS vs interlaced array

Script Preparation code:

var N = 1000000

var x = new Float32Array(N);
var y = new Float32Array(N);
var z = new Float32Array(N);

var interlaced = new Float32Array(3*N);

var vectors = [];

for (var i = 0, li=x.length; i < li; ++i) {
	x[i] = Math.random();
	y[i] = Math.random();
	z[i] = Math.random();
	vectors.push( {x:Math.random(), y:Math.random(), z:Math.random()} );
}

for (var i = 0, li=interlaced.length; i < li; ++i) {
	interlaced[i] = Math.random();
}

Tests:

AoS
var vector; for (var i = 0, li=vectors.length; i < li; ++i) { vector = vectors[i]; vector.x = 2 * vector.x; vector.y = 2 * vector.y; vector.z = 2 * vector.z; }
SoA
for (var i = 0, li=x.length; i < li; ++i) { x[i] = 2 * x[i]; y[i] = 2 * y[i]; z[i] = 2 * z[i]; }

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
AoS
SoA

Fastest: N/A

Slowest: N/A

Latest run results:

No previous run results

This benchmark does not have any results yet. Be the first one to run it!

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

LLMs can make mistakes. Check important info.

Let's break down the benchmark and its components.

**Benchmark Name:** Multiplying lists of vectors - SoA vs AoS vs interlaced array

The benchmark compares three different approaches to perform multiplication operations on lists of vectors:

1. **SoA (Single Array of Structured Data)**: In this approach, all vectors are stored in a single Float32Array.
2. **AoS (Array of Single Arrays)**: Each vector is stored in its own separate Float32Array.
3. **Interlaced Array**: The benchmark also tests an interlaced array approach, where the three components of each vector are stored in consecutive elements of a single Float32Array.

**Options Comparison**

* SoA vs AoS:
	+ Pros: Reduced memory allocation and garbage collection overhead for large datasets. All data is accessed sequentially, which can lead to better cache locality.
	+ Cons: More complex iteration logic, as the developer needs to keep track of the individual vectors' indices.
* Interlaced Array:
	+ Pros: Similar to SoA in terms of reduced memory allocation and garbage collection overhead, but with less complex iteration logic. The interlaced array approach can also lead to better cache locality due to the contiguous storage of vector components.
	+ Cons: More challenging to implement, as the developer needs to manage the three components of each vector.

**Library and Special JS Features**

The benchmark uses the `Float32Array` class from the Web API, which is a typed array used for efficient storage and manipulation of 32-bit floating-point numbers. There are no special JavaScript features or syntax used in this benchmark.

**Other Considerations**

* The benchmark assumes that the vectors' components will be randomly generated during execution, so the developer needs to consider the performance impact of random number generation.
* The benchmark may not accurately represent real-world scenarios where vectors have a fixed structure and size.

**Alternatives**

If you need to implement similar benchmarks for other data structures or operations, consider exploring:

* **Multi-Dimensional Arrays**: Similar to interlaced arrays, but with more complex indexing and iteration logic.
* **Spatial Hashing**: A technique used in games and simulations to efficiently store and query large numbers of objects in 2D or 3D space.
* **GPU-accelerated Benchmarks**: Consider using frameworks like WebGL or CUDA to measure the performance impact of GPU acceleration on your benchmark.

These alternatives will help you understand the trade-offs between different data structures and algorithms, which is essential for developing efficient software solutions.

Related benchmarks:

Multiplying lists of vectors - SoA vs AoS vs interlaced array (version: 2)

Comparing performance of: AoS vs SoA

Created: 9 years ago by: Registered User

Jump to the latest result

AoS

SoA

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):