Benchmark: DataView vs TypedArray Float64

Script Preparation code:

var buffer1 = new ArrayBuffer(1024 * Float64Array.BYTES_PER_ELEMENT);
var array = new Float64Array(buffer1);
var buffer2 = new ArrayBuffer(1024 * Float64Array.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.setFloat64(i, 1); } else { view.setFloat64(i, view.getFloat64(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.setFloat64(i, 1); } else { view.setFloat64(i, view.getFloat64(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: 11 months ago)

User agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/140.0.0.0 Safari/537.36 Edg/140.0.0.0

Browser/OS: Chrome 140 on Windows

View result in a separate tab

array[i] = i;

61K/s

array[i] = i; 2

25K/s

view.setFloat64(i, i) 2

18K/s

view.setFloat64(i, i)

16K/s

View exact numbers

Test name	Executions per second
✓ array[i] = i;	60,622 Ops/sec
array[i] = i; 2	24,610 Ops/sec
view.setFloat64(i, i) 2	18,397 Ops/sec
view.setFloat64(i, i)	16,351 Ops/sec

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

LLMs can make mistakes. Check important info.

The benchmark outlined in the provided JSON compares the performance of two different approaches to handling numerical data in JavaScript: using `TypedArray` (specifically `Float64Array`) versus using `DataView`.

### Approaches Compared:

1. **TypedArray (Float64Array)**:
   - **Test Cases**: 
     - Test: `array[i] = i;`
     - Test: `array[i] = i; 2`
   - **Description**: The `Float64Array` is a typed array that represents an array of 64-bit floating-point numbers. It provides a way to handle binary data and allows for efficient storage and manipulation of numerical data.

2. **DataView**:
   - **Test Cases**: 
     - Test: `view.setFloat64(i, i)`
     - Test: `view.setFloat64(i, i) 2`
   - **Description**: The `DataView` object allows for read and write operations with buffers that have a defined byte order. It provides a more flexible way to manipulate binary data compared to typed arrays, allowing you to work with various data types in a single buffer.

### Performance Measurement:

The benchmarks measure the number of executions per second achieved by each approach:

- **TypedArray**:
  - `array[i] = i;`: 306,217.91 executions per second
  - `array[i] = i; 2`: 305,752.81 executions per second
  
- **DataView**:
  - `view.setFloat64(i, i)`: 274,843.56 executions per second
  - `view.setFloat64(i, i) 2`: 273,932.09 executions per second
  
### Pros and Cons:

#### TypedArray (Float64Array):
- **Pros**:
  - Optimized for performance with operations on numerical data.
  - More predictable performance characteristics due to its contiguous memory structure.
  - Simpler syntax for operations on arrays of numeric types.

- **Cons**:
  - Limited to Type-specific handling (in this case, only 64-bit floats).
  - Less flexible than DataView when interacting with mixed data types or byte order specifications.

#### DataView:
- **Pros**:
  - Allows operations on various data types from a single buffer, enhancing flexibility.
  - A more detailed control over byte ordering (endianness) and types, beneficial for specific applications (e.g., reading from binary files).

- **Cons**:
  - Potentially slower than TypedArray for pure numeric operations because of the extra complexity in handling different types and the operations that manage byte offsets.

### Other Considerations:

- **Use Cases**: 
  - Use `TypedArray` when operations primarily involve numerical calculations, as it generally delivers better performance.
  - Use `DataView` when working with mixed data types or binary formats where memory layout needs to be explicitly controlled.
  
- **Alternatives**:
  - For non-numerical data manipulation, standard JavaScript arrays may suffice, but they lack the performance optimizations provided by typed arrays.
  - WebAssembly (Wasm) could also be used for performance-critical applications where direct memory manipulation and cross-language compatibility are needed.
  
In summary, while `TypedArray` generally provides better performance for straightforward numeric operations, `DataView` offers more flexibility for handling various data types and structures within a single buffer. The choice between them should depend on the specific requirements of the application in terms of performance and data handling capabilities.

DataView vs TypedArray Float64 (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 year ago by: Guest

Go 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>

Fastest: N/A

Slowest: N/A

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

DataView vs TypedArray Float64 (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 year ago by: Guest

Go 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>

Fastest: N/A

Slowest: N/A

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

Related benchmarks

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