Benchmark: soa vs aos vs typed soa

Script Preparation code:

var N = 1000000;

var x = new Array(N), y = new Array(N), vx = new Array(N), vy = new Array(N);

var buffer = new ArrayBuffer(N*8*4);

var tx = new Float64Array(buffer, 0, N),
    ty = new Float64Array(buffer, N*8, N),
    tvx = new Float64Array(buffer, N*16, N),
    tvy = new Float64Array(buffer, N*24, N);

var vectors = new Array(N);

for(var i = 0; i < N; ++i){
  x[i] = tx[i] = Math.random()*100|0;
  y[i] = ty[i] = Math.random()*100|0;
  vx[i] = tvx[i] = Math.random()*100|0;
  vy[i] = tvy[i] = Math.random()*100|0;
  
  vectors[i] = { x: x[i], y: y[i], vx: vx[i], vy: vy[i] };
}

var vector;

Tests:

soa
for (var i = 0; i < N; ++i) { x[i] += vx[i]; y[i] += vy[i]; }
aos
for (var i = 0; i < N; ++i) { vector = vectors[i]; vector.x += vector.vx; vector.y += vector.vy; }
typed soa
for (var i = 0; i < N; ++i) { tx[i] += tvx[i]; ty[i] += tvy[i]; }

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
soa
aos
typed soa

Fastest: N/A

Slowest: N/A

Autogenerated LLM Summary (model llama3.2:3b, generated 27 days ago):

LLMs can make mistakes. Check important info.

I'll break down the provided benchmark and explain what's being tested, compared, and their pros and cons.

**Benchmark Overview**

The test is designed to compare three different approaches for updating vectors in an array:

1. **SoA (Structured Array)**: The vector components are stored in separate arrays (`x`, `y`, `vx`, and `vy`) and then updated using these individual arrays.
2. **AoS (Unstructured Array)**: A single array is used to store all the vector components, and updates are performed directly on this array.
3. **Typed SoA**: An ArrayBuffer is created, which stores a typed representation of the vectors. The component values are stored in separate arrays (`tx`, `ty`, `tvx`, and `tyy`) within the buffer.

**Options Compared**

The benchmark compares the performance of these three approaches:

* **SoA (Structured Array)**: Updates individual array components (`x[i] += vx[i];`, `y[i] += vy[i];`)
* **AoS (Unstructured Array)**: Updates a single array directly (`vector = vectors[i]; vector.x += vector.vx;`)

**Pros and Cons**

Here's a summary of the pros and cons for each approach:

1. **SoA (Structured Array)**
	* Pros:
		+ Easier to understand and maintain, as updates are performed on individual components.
		+ May be more efficient due to the ability to use optimized array operations.
	* Cons:
		+ Requires more memory allocation and deallocation, which can lead to performance overhead.
2. **AoS (Unstructured Array)**
	* Pros:
		+ Less memory allocation and deallocation, potentially leading to better performance.
	* Cons:
		+ Updates are performed on a single array, making it harder to debug and maintain.
		+ May not be as efficient due to the need for indexing and dereferencing operations.
3. **Typed SoA**
	* Pros:
		+ Provides a more compact representation of the vectors in memory.
	* Cons:
		+ More complex and potentially harder to understand, especially for developers without experience with typed arrays.

**Library Usage**

The test case uses JavaScript's `Float64Array` and `ArrayBuffer` APIs to create the typed array. These libraries are part of the modern web platform and provide efficient ways to work with floating-point numbers and other types in JavaScript.

**Special JS Feature/Syntax**

There is no explicit mention of any special JavaScript features or syntax used in this benchmark. However, it's worth noting that the use of `let` and `const` keywords (not shown in the provided code) would be considered a best practice for modern JavaScript development.

**Other Alternatives**

If the developer wants to explore alternative approaches or optimization techniques, they might consider:

* **SIMD (Single Instruction, Multiple Data)**: Using SIMD instructions can provide significant performance benefits for vectorized operations.
* **GPU Acceleration**: Utilizing GPU acceleration can further improve performance by offloading computations to dedicated hardware.
* **Cache Optimization**: Optimizing cache usage and memory access patterns can help reduce memory access latency and improve overall system performance.

These alternatives might require more significant changes to the codebase, but they can potentially provide even better performance and efficiency.

Latest run results:

Run details: (Test run date: 10 days ago)

User agent: Mozilla/5.0 (iPhone; CPU iPhone OS 17_7_1 like Mac OS X) AppleWebKit/605.1.15 (KHTML, like Gecko) Version/17.8 Mobile/15E148 Safari/604.1

Browser/OS: Mobile Safari 17 on iOS 17.7.1

View result in a separate tab

Test name	Executions per second
soa	2.2 Ops/sec
aos	1.6 Ops/sec
typed soa	2.1 Ops/sec

Related benchmarks:

soa vs aos vs typed soa (version: 0)

As titled

Comparing performance of: soa vs aos vs typed soa

Created: 7 years ago by: Guest

Jump to the latest result

soa

aos

typed soa

Suite status: <idle, ready to run>

Experimental features:

Fastest: N/A

Slowest: N/A

Autogenerated LLM Summary (model llama3.2:3b, generated 27 days ago):