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

Multiplying lists of vectors - SoA vs AoS vs interlaced array (version: 0)

What's the fastest way to process a list of vectors? Do you go the OO route, with an array of Vector() objects? Do you create a new data structure, using Float32Arrays for x, y, and z? Do you create a single Float32Array and interlace values for x, y, and z within it? Let's find out!

Comparing performance of: AoS vs SoA - one loop vs SoA - one component per loop vs Interlaced Array - no loop unrolling vs Interlaced Array - with loop unrolling vs AoS (bracket notation)

Created: 8 years ago by: Guest

Jump to the latest result

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 - one loop
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]; }
SoA - one component per loop
for (var i = 0, li=x.length; i < li; ++i) { x[i] = 2 * x[i]; } for (var i = 0, li=y.length; i < li; ++i) { y[i] = 2 * y[i]; } for (var i = 0, li=z.length; i < li; ++i) { z[i] = 2 * z[i]; }
Interlaced Array - no loop unrolling
for (var i = 0, li=interlaced.length; i < li; ++i) { interlaced[i] = 2*interlaced[i]; }
Interlaced Array - with loop unrolling
for (var i = 0, li=interlaced.length; i < li; i+=3) { interlaced[i] = 2*interlaced[i]; interlaced[i+1] = 2*interlaced[i+1]; interlaced[i+2] = 2*interlaced[i+2]; }
AoS (bracket notation)
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']; }

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
AoS
SoA - one loop
SoA - one component per loop
Interlaced Array - no loop unrolling
Interlaced Array - with loop unrolling
AoS (bracket notation)

Fastest: N/A

Slowest: N/A

Autogenerated LLM Summary (model llama3.2:3b, generated 26 days ago):

LLMs can make mistakes. Check important info.

I'll provide an explanation of the benchmark and its various aspects.

**Benchmark Definition**

The benchmark is designed to measure the performance of different approaches for multiplying lists of vectors in JavaScript. The test case creates a list of 1,000,000 random vectors and multiplies each vector by 2 using different methods:

1. **Array of Objects (AoS)**: Each vector is an object with x, y, and z properties.
2. **Structured Array (SoA)**: Vectors are stored in separate arrays for x, y, and z components.
3. **Interlaced Array**: A single array stores the values for all three components, interleaved.

**Options Compared**

The benchmark compares four variants of AoS and SoA:

1. **One Loop (SoA)**: All vector components are multiplied in a single loop.
2. **One Component Per Loop (SoA)**: Each component is multiplied separately in different loops.
3. **Interlaced Array - No Loop Unrolling**: The values for all three components are stored in a single array, but no loop unrolling is used.
4. **Interlaced Array - With Loop Unrolling**: The values for all three components are stored in a single array, and the loop unrolls to process each component in parallel.

**Pros and Cons**

1. **Array of Objects (AoS)**:
	* Pros: Easier to implement, more flexible.
	* Cons: May incur additional overhead due to property access.
2. **Structured Array (SoA)**:
	* Pros: Can be faster for large datasets, reduces memory allocation/deallocation.
	* Cons: More complex implementation, may require explicit bounds checking.
3. **Interlaced Array**:
	* Pros: Can be more efficient in terms of memory usage and cache locality.
	* Cons: May have higher overhead due to loop unrolling and data access patterns.

**Latest Benchmark Result**

The benchmark result shows the executions per second (FPS) for each test case on a specific device (Firefox 49, Windows 7). The top performer is **Interlaced Array - With Loop Unrolling**, followed closely by **Structured Array (SoA)**. The AoS variants trail behind, with the "bracket notation" variant performing slightly better than the others.

**Conclusion**

The benchmark highlights the importance of data structure choice and implementation details in achieving optimal performance. Interlacing arrays can provide a significant performance boost due to improved cache locality, but may require more complex loop unrolling logic. The best approach will depend on specific use cases and requirements.

Latest run results:

Run details: (Test run date: 8 years ago)

User agent: Mozilla/5.0 (Windows NT 6.1; WOW64; rv:49.0) Gecko/20100101 Firefox/49.0

Browser/OS: Firefox 49 on Windows 7

View result in a separate tab

Test name	Executions per second
AoS	97.3 Ops/sec
SoA - one loop	32.3 Ops/sec
SoA - one component per loop	53.0 Ops/sec
Interlaced Array - no loop unrolling	52.5 Ops/sec
Interlaced Array - with loop unrolling	53.8 Ops/sec
AoS (bracket notation)	94.1 Ops/sec

Related benchmarks:

Multiplying lists of vectors - SoA vs AoS vs interlaced array (version: 0)

What's the fastest way to process a list of vectors? Do you go the OO route, with an array of Vector() objects? Do you create a new data structure, using Float32Arrays for x, y, and z? Do you create a single Float32Array and interlace values for x, y, and z within it? Let's find out!

Comparing performance of: AoS vs SoA - one loop vs SoA - one component per loop vs Interlaced Array - no loop unrolling vs Interlaced Array - with loop unrolling vs AoS (bracket notation)

Created: 8 years ago by: Guest

Jump to the latest result

AoS

SoA - one loop

SoA - one component per loop

Interlaced Array - no loop unrolling

Interlaced Array - with loop unrolling

AoS (bracket notation)

Suite status: <idle, ready to run>

Experimental features:

Fastest: N/A

Slowest: N/A

Autogenerated LLM Summary (model llama3.2:3b, generated 26 days ago):