Benchmark: DataView vs TypedArray Float32 actually fixed

Script Preparation code:

var buffer1 = new ArrayBuffer(1024 * Float32Array.BYTES_PER_ELEMENT);
var array = new Float32Array(buffer1);
var buffer2 = new ArrayBuffer(1024 * Float32Array.BYTES_PER_ELEMENT);
var view = new DataView(buffer2);

Tests:

array[i] = i;
for (let i = 0; i < 1024; i += 1) { if (i === 0) { array[i] = 1; } else { array[i] = array[i-1] + i;; } }
view.setFloat32(i, i)
for (let i = 0; i < 4096; i += 4) { if (i === 0) { view.setFloat32(i, 1); } else { view.setFloat32(i, view.getFloat32(i - 1) + i); } }
array[i] = i; 2
for (let i = 0; i < 1024; i += 1) { if (i === 0) { array[i] = 1; } else { array[i] = array[i-1] + i; } }
view.setFloat32(i, i) 2
for (let i = 0; i < 4096; i += 4) { if (i === 0) { view.setFloat32(i, 1); } else { view.setFloat32(i, view.getFloat32(i - 1) + i); } }

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
array[i] = i;
view.setFloat32(i, i)
array[i] = i; 2
view.setFloat32(i, i) 2

Fastest: N/A

Slowest: N/A

Latest run results:

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

User agent: Mozilla/5.0 (iPhone; CPU iPhone OS 18_3_2 like Mac OS X) AppleWebKit/605.1.15 (KHTML, like Gecko) Version/18.3.1 Mobile/15E148 Safari/604.1

Browser/OS: Mobile Safari 18 on iOS 18.3.2

View result in a separate tab

Test name	Executions per second
array[i] = i;	171252.5 Ops/sec
view.setFloat32(i, i)	109994.3 Ops/sec
array[i] = i; 2	171448.1 Ops/sec
view.setFloat32(i, i) 2	109998.0 Ops/sec

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

LLMs can make mistakes. Check important info.

The benchmark defined in the provided JSON compares two different approaches to manipulating binary data in JavaScript: using `Float32Array` (a typed array) and using `DataView`. Here’s a detailed examination of what is tested, the alternatives considered, pros and cons, and the associated libraries.

### Benchmark Overview

#### Context
- The benchmark is designed to test performance in manipulating arrays of floating-point numbers (specifically 32-bit floats) using two methods:
  1. **Typed Arrays** (`Float32Array`)
  2. **DataView**

### Options Compared

1. **Float32Array**
   - **Description**: A typed array that represents an array of 32-bit floating-point numbers. It provides a way to handle binary data directly.
   - **Implementation**: 
     - Code example: `array[i] = array[i-1] + i;`
   - **Pros**:
     - Optimized for performance. Operations on typed arrays can be faster because they leverage the underlying hardware more directly.
     - Easier syntax for accessing and manipulating elements.
     - Provides built-in functionality for handling numerical types efficiently.
   - **Cons**:
     - Less flexible regarding the handling of mixed data types (only floats in this case).
     - Memory layout is strictly linear and can be less conducive to complex structures.

2. **DataView**
   - **Description**: A low-level interface for reading and writing multiple number types (like Int16, Float32, etc.) from an `ArrayBuffer`, enhancing flexibility.
   - **Implementation**: 
     - Code example: `view.setFloat32(i, view.getFloat32(i - 1) + i);`
   - **Pros**:
     - More versatile; allows reading and writing different data types directly on the same buffer.
     - Useful for manipulating binary data from external sources where specific data formats are diverse.
   - **Cons**:
     - Potentially slower than typed arrays due to the overhead of method calls (`setFloat32`, `getFloat32`).
     - Requires more verbose syntax, which can lead to less readable code for simple numerical tasks.

### Test Cases

Four different test cases were executed,:
1. Two cases using `Float32Array`:
   - `array[i] = array[i - 1] + i;` executed for 1024 iterations.
   
2. Two cases using `DataView`:
   - `view.setFloat32(i, view.getFloat32(i - 1) + i);` executed for 4096 iterations (with a step of 4).

The repetition of test cases with varying counts (and slightly altered implementations) allows for testing the influence of both the array structure and the operation count on performance.

### Results Summary

The results show that operations on `Float32Array` are significantly faster than those using `DataView`:
- `Float32Array` results exhibit execution speeds around 237,000 executions per second.
- `DataView` results exhibit execution speeds around 64,000 executions per second.

### Other Considerations

- **Memory Management**: Both methods use `ArrayBuffer` for underlying memory, but the choice of interface (typed array vs DataView) will influence memory usage patterns and efficiency.
- **Use Cases**: Choose `Float32Array` for straightforward numerical computations and performance-critical applications. Use `DataView` when data manipulation requires mixed data types or complex data structures.
  
### Alternatives

- **Regular Arrays**: In scenarios where performance isn't critical and other data types are involved, regular JavaScript arrays may suffice but come at a significant performance cost when dealing with large numbers of numerical operations.
- **WebAssembly**: For heavy computation tasks, consider using WebAssembly for maximum performance, especially for numeric-heavy applications where languages like Rust or C can be compiled to WebAssembly.
  
### Conclusion

This benchmark provides key insights into the performance trade-offs between using `Float32Array` and `DataView` in JavaScript. It highlights that while both have their places in different applications, `Float32Array` typically offers better performance for numerical tasks solely focused on floating-point arithmetic.

Related benchmarks:

DataView vs TypedArray Float32 actually fixed (version: 1)

Comparing performance of: array[i] = i; vs view.setFloat32(i, i) vs array[i] = i; 2 vs view.setFloat32(i, i) 2

Created: one month ago by: Guest

Jump to the latest result

array[i] = i;

view.setFloat32(i, i)

array[i] = i; 2

view.setFloat32(i, i) 2

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):