Benchmark: js length vs cached length

Script Preparation code:

var i = 0, count = 30000, a;
var arr = []

for (i = 0; i < count; i++) {
  if (Math.random() > 0.5) {
    arr.push(i * i);
  }
}

Tests:

Map 1
for (i = 0; i < arr.length; i++) { a = arr[i]; }
Map 2
arrLength = arr.length for (i = 0; i < arrLength; i++) { a = arr[i]; }

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
Map 1
Map 2

Fastest: N/A

Slowest: N/A

Latest run results:

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Autogenerated LLM Summary (model llama3.2:3b, generated one year ago):

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I'll break down the provided benchmark JSON and explain what's being tested, compared options, pros and cons of each approach, and other considerations.

**Benchmark Definition:**

The benchmark measures the performance difference between two approaches to accessing an array in JavaScript:

1. **Using `arr.length`**: This approach retrieves the length of the array using the property and then uses it as a loop counter.
2. **Using a mapped loop**: This approach uses a mapped loop, where each iteration of the outer loop assigns the current element of the array to a variable `a`.

**Script Preparation Code:**

The script preparation code creates an array `arr` with 30,000 elements, half of which are filled with perfect squares.

**Options Compared:**

Two options are being compared:

1. **`Map 1`: Using `arr.length`**: This approach uses the length property of the array to create a loop counter.
2. **`Map 2`: Mapped Loop**: This approach uses a mapped loop, where each iteration assigns the current element of the array to a variable `a`.

**Pros and Cons of Each Approach:**

1. **`Map 1`: Using `arr.length`**
	* Pros:
		+ Easy to understand and implement.
		+ Can be optimized using caching or memoization techniques.
	* Cons:
		+ May incur additional overhead due to property access.
		+ Limited cache locality, leading to slower performance.
2. **`Map 2`: Mapped Loop**
	* Pros:
		+ Good cache locality, reducing memory accesses and improving performance.
		+ Can be optimized using loop unrolling or other parallelization techniques.
	* Cons:
		+ More complex implementation compared to `Map 1`.
		+ May require additional setup and configuration.

**Library and Special JS Feature:**

There is no library being used in this benchmark, but it does utilize a JavaScript feature called **loop unrolling**, which involves increasing the number of iterations executed within a single loop iteration. This can lead to better cache locality and improved performance.

**Other Considerations:**

* **Array size**: The benchmark uses an array with 30,000 elements, which is relatively large. A smaller array might not exhibit significant differences between these two approaches.
* **Browser and platform**: The benchmark result shows the performance difference for a specific browser (Opera 100) on a desktop platform (Mac OS X 10.15.7). Different browsers or platforms may exhibit different performance characteristics.
* **Alternative Approaches**: Other alternatives to consider might include:
	+ Using `at()` method instead of `arr.length` for array indexing.
	+ Using `Array.prototype.forEach()` instead of a mapped loop.

**Alternatives:**

If you're interested in exploring other approaches or optimization techniques, here are some potential alternatives:

* **Using caching**: Store the length property value once and reuse it throughout the loop to reduce overhead.
* **Loop unrolling**: Increase the number of iterations executed within a single loop iteration using techniques like tail recursion or loop fusion.
* **Parallelization**: Use parallel processing techniques, such as worker threads or Web Workers, to execute multiple loops concurrently.

These alternatives can potentially improve performance but may require additional setup and configuration.

Related benchmarks:

js length vs cached length (version: 0)

js length vs cached length

Comparing performance of: Map 1 vs Map 2

Created: 2 years ago by: Guest

Jump to the latest result

Map 1

Map 2

Suite status: <idle, ready to run>

Fastest: N/A

Slowest: N/A

No previous run results

Autogenerated LLM Summary (model llama3.2:3b, generated one year ago):