Benchmark: sum with typed int array vs standard array

HTML Preparation code:

<!--your preparation HTML code goes here-->

Script Preparation code:

function sumWithReduce(arr) {
    return arr.reduce((x, y) => (y = +y) ? x + y: x, 0)
}

function sumWithReduceInt16(arr) {
    return arr.reduce((x, y) => x + y, 0)
}

function sumWithLoop(arr) {
    let length = arr.length;
    let init = 0;
    let val;
    
    while (length--)
        if (val = +arr[length])
            init += val;
    
    return init; 
}

function sumWithLoopInt16(arr) {
    let length = arr.length;
    let init = 0;
    let val;
    
    while (length--)
    	init += val;
    
    return init; 
}

const nums = Array.from({length: 10000}, () => Math.floor(Math.random() * 65536))
// throw in some non numbers
nums[4] = '5'; nums[22] = NaN;
// coerces non numbers to zero
const numsInt = new Uint16Array(nums);

Tests:

Sum with 16-bit array and loop
sumWithLoopInt16(numsInt)
Sum with standard array and reduce
sumWithReduce(nums)
Sum with standard array and loop
sumWithLoop(nums)
Sum with 16-bit array and reduce
sumWithReduceInt16(numsInt)

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
Sum with 16-bit array and loop
Sum with standard array and reduce
Sum with standard array and loop
Sum with 16-bit array and reduce

Fastest: N/A

Slowest: N/A

Latest run results:

Run details: (Test run date: 20 days ago)

User agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:133.0) Gecko/20100101 Firefox/133.0

Browser/OS: Firefox 133 on Windows

View result in a separate tab

Test name	Executions per second
Sum with 16-bit array and loop	87659.8 Ops/sec
Sum with standard array and reduce	12308.9 Ops/sec
Sum with standard array and loop	16043.6 Ops/sec
Sum with 16-bit array and reduce	6839.9 Ops/sec

Autogenerated LLM Summary (model gpt-4o-mini, generated 20 days ago):

LLMs can make mistakes. Check important info.

The provided JSON represents a benchmark that compares the performance of two different array types and two different summation methods in JavaScript. The benchmark specifically tests the following approaches:

### Options Compared

1. **16-bit Typed Array with Loop** (`sumWithLoopInt16`)
   - This function sums the elements of a `Uint16Array` using a standard loop.
   
2. **Standard Array with Loop** (`sumWithLoop`)
   - This function sums the elements of a regular JavaScript array using a standard loop.
   
3. **Standard Array with Reduce** (`sumWithReduce`)
   - This function sums the elements of a regular JavaScript array using the `reduce()` method.
   
4. **16-bit Typed Array with Reduce** (`sumWithReduceInt16`)
   - This function sums the elements of a `Uint16Array` using the `reduce()` method.

### Pros and Cons

#### Typed Arrays (e.g., `Uint16Array`)
- **Pros:**
  - **Memory Efficiency**: Typed arrays provide a mechanism to represent binary data in a more compact manner than standard arrays. `Uint16Array` specifically allocates memory for 16-bit unsigned integers, which can reduce memory usage compared to regular arrays that can hold any type of data.
  - **Performance Gains**: They offer better performance for numerical operations because they work on uniform types and avoid type coercion and checks.

- **Cons:**
  - **Type Constraints**: Typed arrays are fixed to a specific data type and require careful management of data types, which could lead to issues if the data types are not consistently provided.
  
#### Standard Arrays
- **Pros:**
  - **Flexibility**: Standard arrays can hold elements of any type, including objects, numbers, strings, etc.
  - **Ease of Use**: They offer a broader range of built-in methods (e.g., `map`, `filter`, `reduce`) that make data manipulation straightforward.

- **Cons:**
  - **Performance Issues**: Due to their flexibility, standard arrays perform worse in numerical calculations compared to typed arrays. Operations that involve frequent type coercion can slow down performance.

### Functional Techniques Compared

1. **Loop vs. Reduce**:
   - **Loop (both standard and typed arrays)**: Generally more performant for large datasets because it doesn't involve the additional overhead of a function call on each element.
   - **Reduce**: Provides a more declarative and arguably cleaner approach, especially for operations involving transformations. However, it may have slight overhead due to function invocations.

### Other Considerations

- **Random Data Generation**: In the preparation code, both the standard array (`nums`) and the typed array (`numsInt`) are populated with random numbers ranging from 0 to 65535, with a couple of non-numeric entries thrown in for realism. This simulates real-world data where input data might not always be clean, affecting how the various methods handle non-numeric values (numeric coercion in this case using `+`).
  
- **Coercion Handling**: The summation methods include handling of non-numeric values, with the `reduce` method in standard arrays coerced non-numeric values to `0`, whereas the loop in both cases uses conditional logic to manage this.

### Alternative Approaches

- **Using `forEach`**: Instead of using `for` loops or `reduce`, JavaScript's `forEach` could also be employed for summation, providing a more functional approach, although likely with worse performance than traditional loops.

- **Web Workers**: For large data sets, summation tasks could be offloaded to Web Workers, enabling parallel processing and potentially improving performance.

- **Different Typed Arrays**: Other typed arrays like `Float32Array` or `Int32Array` could be explored for different precision and range requirements in numerical calculations.

In conclusion, the benchmark tests differing performances among typed and standard arrays, along with different summation methods, taking into consideration both the approach chosen for manipulation of the array and how JavaScript handles data types internally. This offers valuable insights for developers deciding on the optimal approach for numerical operations in their applications.

Related benchmarks:

sum with typed int array vs standard array (version: 3)

testing performance of type arrays vs standard arrays

Comparing performance of: Sum with 16-bit array and loop vs Sum with standard array and reduce vs Sum with standard array and loop vs Sum with 16-bit array and reduce

Created: 20 days ago by: Registered User

Jump to the latest result

Sum with 16-bit array and loop

Sum with standard array and reduce

Sum with standard array and loop

Sum with 16-bit array and reduce

Suite status: <idle, ready to run>

Experimental features:

Fastest: N/A

Slowest: N/A

Autogenerated LLM Summary (model gpt-4o-mini, generated 20 days ago):