Benchmark: JS Build Big Array

Script Preparation code:

var n = 10000000

Tests:

Dynamic
const arr = []; for (let i = 0; i < n; i++) { arr.push(i); }
Pre Allocated
const arr = new Array(n); for (let i = 0; i < n; i++) { arr[i] = i; }

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
Dynamic
Pre Allocated

Fastest: N/A

Slowest: N/A

Autogenerated LLM Summary (model llama3.2:3b, generated 27 days ago):

LLMs can make mistakes. Check important info.

Let's break down the benchmark and explain what is being tested.

**Benchmark Overview**

The benchmark, titled "JS Build Big Array", measures the performance of JavaScript arrays in different ways. The script preparation code sets the `n` variable to 10 million, which will be used as the size of the array being built.

**Script Preparation Code Options**

There are two options for preparing the script:

1. **Dynamic**: This option uses a dynamic array creation method, where the array is created and populated in a single loop.
```javascript
const arr = [];
for (let i = 0; i < n; i++) {
  arr.push(i);
}
```
2. **Pre-Allocated**: This option creates an array with a fixed size of `n` elements before populating it.
```javascript
const arr = new Array(n);
for (let i = 0; i < n; i++) {
  arr[i] = i;
}
```
**Comparison**

The comparison between these two approaches is mainly in terms of memory allocation and the overhead of creating an array with a fixed size versus dynamically allocating it.

**Pros and Cons**

1. **Dynamic Approach**
	* Pros:
		+ Less memory allocation overhead, as no extra memory needs to be allocated upfront.
		+ May result in better performance for smaller arrays or when resources are limited.
	* Cons:
		+ Can lead to slower performance due to the overhead of creating an array and pushing elements onto it.
		+ May not be suitable for large datasets where memory allocation can become a bottleneck.
2. **Pre-Allocated Approach**
	* Pros:
		+ Faster performance, as the array is already allocated with the desired size.
		+ Suitable for large datasets where memory allocation can become a bottleneck.
	* Cons:
		+ More memory allocation overhead upfront.
		+ May not be suitable for smaller arrays or when resources are limited.

**Other Considerations**

1. **Browser and Platform Variance**: The benchmark results show significant variance between browsers (Chrome 119) and platforms (Desktop vs. unknown). This highlights the importance of testing on different environments to ensure the benchmark is representative.
2. **Library Usage**: The benchmark uses built-in JavaScript features, such as `Array.prototype.push()` and `new Array()`. No external libraries are used in this example.

**Special JS Features or Syntax**

This benchmark does not use any special JavaScript features or syntax that would affect its outcome.

**Alternative Benchmarks**

Other benchmarks could explore similar scenarios, such as:

1. **Array Splice vs. push**: Measures the performance of array modification using `splice()` versus `push()`.
2. **Array Iteration**: Compares different iteration methods (e.g., `for`, `forEach`, `map()`) for populating an array.
3. **Data Structures Comparison**: Tests different data structures, such as arrays, linked lists, or trees, to determine their performance characteristics.

These alternative benchmarks can help provide a more comprehensive understanding of JavaScript array performance and the impact of various optimization techniques on real-world applications.

Latest run results:

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

User agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/119.0.0.0 Safari/537.36

Browser/OS: Chrome 119 on Windows

View result in a separate tab

Test name	Executions per second
Dynamic	2.5 Ops/sec
Pre Allocated	3.2 Ops/sec

Related benchmarks:

JS Build Big Array (version: 1)

Comparing performance of: Dynamic vs Pre Allocated

Created: one year ago by: Registered User

Jump to the latest result

Dynamic

Pre Allocated

Suite status: <idle, ready to run>

Experimental features:

Fastest: N/A

Slowest: N/A

Autogenerated LLM Summary (model llama3.2:3b, generated 27 days ago):