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soa vs aos using Float32Array
(version: 0)
As titled
Comparing performance of:
soa vs aos
Created:
3 years ago
by:
Guest
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Script Preparation code:
var x = new Float32Array(1000000), y = new Float32Array(1000000), z = new Float32Array(1000000); var vectors = []; for (var i = 0; i < 1000000; i++) { x[i] = Math.random() * 100 | 0; y[i] = Math.random() * 100 | 0; z[i] = Math.random() * 100 | 0; vectors[i] = { x: x[i], y: y[i], z: z[i] }; } var vector;
Tests:
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]; }
aos
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>
Run tests (2)
Previous results
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Test case name
Result
soa
aos
Fastest:
N/A
Slowest:
N/A
Latest run results:
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Autogenerated LLM Summary
(model
llama3.2:3b
, generated one year ago):
Let's break down the benchmark and its options. **Benchmark Overview** The benchmark compares two approaches to perform operations on data: 1. **Statically-Ordered Arrays (SOA)**: This approach uses three separate arrays (`x`, `y`, and `z`) to store the data, with each array containing one dimension of the data. 2. **Access-Oblivious Structure (AOS)**: This approach uses an object array (`vectors`) where each element is an object that contains multiple dimensions of the data. **Options Compared** The benchmark compares two specific test cases: 1. `soa`: This test case uses the SOA approach, where three separate arrays are used to store the data. 2. `aos`: This test case uses the AOS approach, where an object array is used to store the data. **Pros and Cons of Each Approach** * **SOA (Statically-Ordered Arrays)**: + Pros: - Can be more efficient for certain types of operations, as the compiler can optimize better. - Typically uses less memory, since each dimension has its own separate array. + Cons: - Requires explicit indexing and bounds checking to access elements in the arrays. - May lead to slower performance due to additional overhead of accessing multiple arrays. * **AOS (Access-Oblivious Structure)**: + Pros: - Can simplify code, as all operations are performed on a single data structure. - Typically uses more memory, since each dimension is stored in the same object. + Cons: - May lead to slower performance due to additional overhead of accessing and manipulating objects. - Requires careful design to ensure efficient access patterns. **Library Used** In this benchmark, no libraries are explicitly mentioned. However, the `Float32Array` constructor is used to create arrays with floating-point data type. This suggests that the benchmark may use Web API (Web Platform Incubator) features for creating and manipulating arrays. **Special JS Features/Syntax** This benchmark does not specifically test any special JavaScript features or syntax. It only focuses on comparing two different approaches to perform operations on data. **Other Alternatives** Some alternative approaches that could be compared in this benchmark include: * **Row-Major Layout**: This approach uses a single array with row-major layout, where each element is accessed by a fixed offset from the previous one. * **Column-Major Layout**: This approach uses a single array with column-major layout, where each element is accessed by a fixed offset from the previous one in the same column. These alternatives could provide additional insights into performance differences and code complexity for different data structures.
Related benchmarks:
Multiplying lists of vectors - SoA vs AoS vs interlaced array
soa vs aos using Float32Array 2
soa vs aos vs typed soa 2
soa vs aos vs typed soa 3
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