Benchmark: For Loop Leng Inside and Outside

Script Preparation code:

var arr = new Array(1000000).fill(0)

Tests:

Inside
for (let i = 0; i < arr.length; i++) {}
Outside
const length = arr.length for (let i = 0; i < length; i++) {}

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
Inside
Outside

Fastest: N/A

Slowest: N/A

Autogenerated LLM Sumamry (model llama3.2:3b, generated 4 days ago):

LLMs can make mistakes. Check important info.

I'd be happy to explain the benchmark.

The provided JSON represents a JavaScript microbenchmark test case on MeasureThat.net. The test compares two approaches: using a traditional for loop with `arr.length` as its condition, and using an "out of bounds" approach where you access an element at index equal to the length of the array (`length`) as the upper bound.

**Traditional For Loop Approach**

The first benchmark definition uses a traditional for loop:
```javascript
for (let i = 0; i < arr.length; i++) {}
```
This approach is widely used in JavaScript and is generally considered efficient. The loop iterates over each element in the array, incrementing the index `i` until it reaches the end of the array.

**Out of Bounds Approach**

The second benchmark definition uses an out of bounds approach:
```javascript
const length = arr.length;
for (let i = 0; i < length; i++) {}
```
In this approach, we first calculate the length of the array and store it in a variable `length`. We then use this value as the upper bound for the loop. This approach is less efficient than the traditional for loop because accessing an element at index equal to the length of the array can lead to unexpected behavior or errors.

**Pros and Cons**

* **Traditional For Loop Approach:**
	+ Pros:
		- More intuitive and readable code.
		- Less prone to errors due to out of bounds access.
	+ Cons:
		- Can be slower for large arrays because of the overhead of calculating `arr.length`.
* **Out of Bounds Approach:**
	+ Pros:
		- Can be faster for very large arrays because it avoids the overhead of calculating `arr.length` on each iteration.
	+ Cons:
		- Less intuitive and readable code.
		- More prone to errors due to out of bounds access.

**Library Usage**

There is no explicit library usage mentioned in this benchmark definition. However, if we assume that `Array.prototype.fill()` is being used to create the array with 1 million elements, it's worth noting that this method is not as widely supported as other methods like `new Array(1000000)`.

**Special JavaScript Features/Syntax**

There are no special JavaScript features or syntax mentioned in this benchmark definition. The code uses standard JavaScript syntax and does not rely on any proprietary or experimental features.

**Other Alternatives**

If you're looking for alternative approaches to this benchmark, here are a few options:

* **Using `for...of` loop:** Instead of using traditional for loops, you could use the `for...of` loop, which is more concise and expressive.
```javascript
for (const element of arr) {
  // code here
}
```
* **Using `Array.prototype.forEach()` method:** This method provides a similar functionality to traditional for loops but with better performance and readability.
```javascript
arr.forEach((element) => {
  // code here
});
```

Keep in mind that these alternatives may not provide the same level of control or performance as the traditional for loop approach, depending on your specific use case.

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/119.0.0.0 Safari/537.36

Browser/OS: Chrome 119 on Windows

View result in a separate tab

Test name	Executions per second
Inside	35.6 Ops/sec
Outside	4425.2 Ops/sec

Related benchmarks:

For Loop Leng Inside and Outside (version: 0)

Comparing performance of: Inside vs Outside

Created: 11 months ago by: Registered User

Jump to the latest result

Inside

Outside

Suite status: <idle, ready to run>

Experimental features:

Fastest: N/A

Slowest: N/A

Autogenerated LLM Sumamry (model llama3.2:3b, generated 4 days ago):