Benchmark: DataView vs TypedArray Float64sssssss

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.setFloat64(i, i)
for (let i = 0; i < 1024; 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.setFloat64(i, i) 2
for (let i = 0; i < 1024; 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.setFloat64(i, i)
array[i] = i; 2
view.setFloat64(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 (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[i] = i;	250837.5 Ops/sec
view.setFloat64(i, i)	286406.7 Ops/sec
array[i] = i; 2	250415.7 Ops/sec
view.setFloat64(i, i) 2	283994.7 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 the performance of two different approaches for handling and manipulating arrays in JavaScript, specifically focusing on a `TypedArray` (Float32Array) and a `DataView`.

### Approaches Compared

1. **TypedArray (Float32Array) Usage**:
   - **Code Example**:
     ```javascript
     for (let i = 0; i < 1024; i += 1) {
       if (i === 0) {
         array[i] = 1;
       } else {
         array[i] = array[i - 1] + i;
       }
     }
     ```

- **Pros**:
     - Typed arrays like `Float32Array` are optimized for use with binary data and are ideal for scenarios requiring high-performance operations on numerical data.
     - They provide methods that are faster than standard arrays for specific types of numerical storage due to their fixed size and type constraints.

- **Cons**:
     - They can be less flexible than regular JavaScript arrays since they only allow specific numeric types (e.g. Float32). If you need to mix types, this approach might not be suitable.
     - Initialization and handling can be a bit more complex if you're not familiar with the typed array handling in JavaScript.

2. **DataView Usage**:
   - **Code Example**:
     ```javascript
     for (let i = 0; i < 1024; i += 4) {
       if (i === 0) {
         view.setFloat32(i, 1);
       } else {
         view.setFloat32(i, view.getFloat32(i - 4) + i);
       }
     }
     ```

- **Pros**:
     - A `DataView` allows reading and writing of multiple types (like Int8, Uint16, Float32, etc.) from the same buffer, giving it more versatility.
     - It provides more control over byte ordering (endianness) which can be crucial when dealing with binary data from external sources.

- **Cons**:
     - Using `DataView` may introduce overhead due to additional method calls (`setFloat32` and `getFloat32`), making it potentially slower than directly accessing a `TypedArray` for numerical calculations, especially if you need to access the same index multiple times.
     - It requires careful management of offsets, which can complicate logic if not handled properly.

### Summary of Test Cases

The benchmark includes four test cases, two for each approach:

- **TypedArray Test Cases**:
  - "array[i] = i;"
  - "array[i] = i; 2"

- **DataView Test Cases**:
  - "view.setFloat64(i, i)"
  - "view.setFloat64(i, i) 2"

Each of these test cases effectively evaluates how quickly each approach can handle a simple operation of populating the buffers with Fibonacci-like sequences.

### Performance Data

The benchmark results showcase the execution speeds:
- DataView methods (`view.setFloat64`) performed slightly better than the TypedArray operations in this specific context, suggesting that for the data access patterns tested (especially when element access intervals are larger), the overhead of DataView access is outweighed by its flexibility and potential optimizations in modern engines.

### Alternatives to Consider

Besides `TypedArray` and `DataView`, other alternatives for handling numerical data in JavaScript include:

1. **Regular Arrays**: They offer flexibility but are generally slower for numerical computation due to lack of optimizations.
   
2. **Libraries**: Libraries like NumPy (linked with Python) provide efficient computation for numerical data and might be considered in a polyglot environment.

3. **Wasmer/Wasm**: WebAssembly offers another route for high-performance applications that require numerical computations, where code written in languages like C/C++ can be executed in the browser with better speed than pure JavaScript implementations.

### Conclusion

The choice between using `TypedArray` and `DataView` largely depends on specific use cases. If performance on fixed numeric types in a straightforward manner is required, `TypedArray` might be the best. Conversely, if there's a need for handling various data types and more control over data serialization, `DataView` would be advantageous. Understanding the trade-offs between these approaches allows developers to choose the most effective method for their performance needs and application context.

Related benchmarks:

DataView vs TypedArray Float64sssssss (version: 1)

Comparing performance of: array[i] = i; vs view.setFloat64(i, i) vs array[i] = i; 2 vs view.setFloat64(i, i) 2

Created: one month ago by: Guest

Jump to the latest result

array[i] = i;

view.setFloat64(i, i)

array[i] = i; 2

view.setFloat64(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):