Benchmark: math on array vs float32array vs float64array - v2

Script Preparation code:

var a = [...Array(10000)].map(_ => Math.random());
var av2 = [...Array(10000)].map(_ => Math.random());
var ta32 = (new Float32Array(10000)).map(_ => Math.random());
var ta32v2 = (new Float32Array(10000)).map(_ => Math.random());
var ta64 = (new Float64Array(10000)).map(_ => Math.random());
var ta64v2 = (new Float64Array(10000)).map(_ => Math.random());

Tests:

array dot
let sum = 0.0; for (let i = 0; i < 10000; ++i) { sum += a[i] * av2[i]; }
Float32Array dot
let sum = 0.0; for (let i = 0; i < 10000; ++i) { sum += ta32[i] * ta32v2[i]; }
Float64Array dot
let sum = 0.0; for (let i = 0; i < 10000; ++i) { sum += ta64[i] * ta64v2[i]; }

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
array dot
Float32Array dot
Float64Array dot

Fastest: N/A

Slowest: N/A

Latest run results:

Run details: (Test run date: one month ago)

User agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/134.0.0.0 Safari/537.36

Browser/OS: Chrome 134 on Windows

View result in a separate tab

Test name	Executions per second
array dot	49320.5 Ops/sec
Float32Array dot	27625.1 Ops/sec
Float64Array dot	24904.7 Ops/sec

Autogenerated LLM Summary (model gpt-4o-mini, generated one month ago):

LLMs can make mistakes. Check important info.

The benchmark you provided tests the performance of different approaches to performing a dot product operation on arrays in JavaScript. The three approaches compared are:

1. **Standard JavaScript Arrays (`array dot`)**
2. **Float32Array (`Float32Array dot`)**
3. **Float64Array (`Float64Array dot`)**

### Benchmark Preparation

The preparation code generates random data for these tests. Each approach is initialized with two separate collections of random numbers:

- `a` and `av2`: Standard JavaScript arrays of 10,000 elements filled with random numbers.
- `ta32` and `ta32v2`: `Float32Array`s, which are typed arrays for storing 32-bit floating-point numbers.
- `ta64` and `ta64v2`: `Float64Array`s, for storing 64-bit floating-point numbers.

### Individual Test Cases

1. **`array dot`**: This test computes the dot product using the standard JavaScript arrays (`a` and `av2`).

**Pros**: 
   - Easy to use and understand.
   - Provides good flexibility and support across environments.

**Cons**:
   - May have slower performance due to type generality and potential boxing of numbers (as they are objects).

2. **`Float32Array dot`**: This test uses `Float32Array` to perform the same dot product operation.

**Pros**:
   - Specifically optimized for numerical calculations, reducing memory usage and potentially improving performance.
   - Better suited for performance-intensive applications, such as graphics and physics simulations, where the precision of 32-bit numbers is sufficient.

**Cons**:
   - Limited range and precision compared to standard numbers and `Float64Array`.
   - Slightly more complex to manage and use, as they require consideration of type constraints.

3. **`Float64Array dot`**: Similar to `Float32Array`, but it uses 64-bit floating-point representation.

**Pros**:
   - Higher precision and a wider range than `Float32Array`, making it suitable for applications where numerical accuracy is critical.

**Cons**:
   - More memory consumption than `Float32Array`, which might not be necessary if the precision is overkill for the application.

### Benchmark Results

The benchmark results show that the different approaches yield varying performance metrics:

- **`array dot`**: 49,320.5 executions per second. This approach performs the best due to the optimizations in modern JavaScript engines.
- **`Float32Array dot`**: 27,625.15 executions per second. Although it offers certain numerical advantages, it lags behind standard arrays in this specific benchmark.
- **`Float64Array dot`**: 24,904.72 executions per second. This approach is the slowest among the three, likely due to its higher precision requirements and associated memory overhead.

### Other Considerations

- **Typed Arrays**: Both `Float32Array` and `Float64Array` are examples of typed arrays in JavaScript, which allow you to work with binary data and improved performance characteristics in numeric calculations compared to regular arrays. They are specifically useful in scenarios that require the handling of large datasets or graphics manipulation.
  
- **Execution Context**: The results may vary based on the underlying JavaScript engine of the browser (in this case, Chrome). Other browsers might have different optimizations for handling arrays or typed arrays.

### Alternatives

- **WebAssembly**: For performance-critical applications, using WebAssembly (Wasm) can offer significant speed improvements over JavaScript. It is especially beneficial for numerical computations, allowing execution of lower-level languages like C/C++ or Rust in the browser.

- **Matrix libraries**: Libraries such as `math.js` or `numeric.js` provide higher-level abstractions for matrix and vector operations and often have underlying optimizations.

In summary, this benchmark demonstrates the trade-offs in performance and precision when utilizing standard arrays versus typed arrays in JavaScript. Depending on the requirements of an application, engineers can choose the most suitable approach to optimize performance and ensure correct functionality.

Related benchmarks:

math on array vs float32array vs float64array - v2 (version: 1)

Comparing performance of: array dot vs Float32Array dot vs Float64Array dot

Created: one month ago by: Registered User

Jump to the latest result

array dot

Float32Array dot

Float64Array dot

Suite status: <idle, ready to run>

Experimental features:

Fastest: N/A

Slowest: N/A

Autogenerated LLM Summary (model gpt-4o-mini, generated one month ago):