Benchmark: very big Array vs TypedArray write performance

Tests:

Array write
var array = new Array(1500000).fill(0); for (let i=0; i<array.length; i++) { array[i] = array[i] + 1; }
TypedArray write
var typedArray = new Uint8Array(1500000).fill(0); for (let i=0; i<typedArray.length; i++) { typedArray[i] = typedArray[i] + 1; }

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
Array write
TypedArray write

Fastest: N/A

Slowest: N/A

Latest run results:

Run details: (Test run date: 4 days ago)

User agent: Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/147.0.0.0 Safari/537.36

Browser/OS: Chrome 147 on Mac OS X 10.15.7

View result in a separate tab

Test name	Executions per second
Array write	547.0 Ops/sec
TypedArray write	949.3 Ops/sec

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

LLMs can make mistakes. Check important info.

The benchmark defined in the provided JSON is focused on measuring the performance of two different data structures in JavaScript: a regular Array and a TypedArray (specifically, a `Uint8Array`). Both are used for storing a sequence of elements, but they have different performance characteristics and use cases.

### Benchmark Overview

1. **Benchmark Name**: Very big Array vs TypedArray write performance
2. **Test Cases**:
   - **Array Write**: Tests the performance of writing (incrementing by 1) to a regular JavaScript Array.
   - **TypedArray Write**: Tests the performance of writing to a `Uint8Array`, which is a type of TypedArray.

### Options Compared

1. **Regular Array**:
   - **Initialization**: The test initializes a standard Array with 1.5 million elements, all set to zero using the `fill()` method. 
   - **Operation**: It iterates over the array and increments each element by 1.

2. **TypedArray (Uint8Array)**:
   - **Initialization**: Similar in initialization to the Array but uses `Uint8Array`, which is a more efficient array type intended for binary data.
   - **Operation**: It also iterates and increments the value of each element.

### Performance Results

From the benchmark results:
- **TypedArray Write**: Achieved approximately **811.12 executions per second**.
- **Array Write**: Achieved approximately **323.38 executions per second**.

### Pros and Cons

#### Regular Array
- **Pros**:
  - More flexible and can store any type of JavaScript object.
  - Natural support for various methods and properties used widely in JavaScript.
  
- **Cons**:
  - Slower performance when handling large datasets, especially for operations like writes and reads, due to additional overhead.
  - Less memory-efficient compared to TypedArrays.

#### TypedArray (Uint8Array)
- **Pros**:
  - Improved performance for numerical data and operations, particularly in large datasets due to optimizations in memory and operations.
  - More memory-efficient as they offer a view into binary data buffers, storing data in a more compact format.
  
- **Cons**:
  - Limited to a specific type of data (in this case, 8-bit unsigned integers).
  - Less flexible since they do not support different data types or higher-level array methods found in regular Arrays.

### Other Considerations

1. **Use Cases**:
   - Regular Arrays are more suitable for general-purpose programming where flexibility is required. 
   - TypedArrays are optimal in scenarios that require handling large volumes of numerical data, such as graphics processing, game development, and data science applications.

2. **Alternatives**:
   - **ArrayBuffer**: A lower-level representation of binary data that can be utilized with TypedArrays to create more complex data structures.
   - **Other TypedArrays**: Besides `Uint8Array`, other variants like `Float32Array`, `Int16Array`, etc., are available, each tailored for different types of numerical data.

3. **Memory Management**:
   - **Garbage Collection**: Regular Arrays can incur more garbage collection overhead due to their dynamic nature, while TypedArrays can lead to less frequent garbage collection cycles.

In conclusion, this benchmark effectively illustrates the significant performance advantages of TypedArrays over traditional Arrays when it comes to numerical data processing. Understanding the trade-offs between flexibility and performance is crucial for developers when selecting the right data structure for their applications.

Related benchmarks:

very big Array vs TypedArray write performance (version: 1)

Comparing performance of: Array write vs TypedArray write

Created: one year ago by: Guest

Jump to the latest result

Array write

TypedArray write

Suite status: <idle, ready to run>

Fastest: N/A

Slowest: N/A

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