Benchmark: slice vs. for - MeasureThat.net

Script Preparation code:

var data = Array.from({length: 100});

Tests:

slice
data.slice(150, 250);
for
let dataFor = []; for (let i = 150; i < 250; i++) { dataFor.push(data[i]); }

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
slice
for

Fastest: N/A

Slowest: N/A

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/114.0.0.0 Safari/537.36

Browser/OS: Chrome 114 on Windows

View result in a separate tab

Test name	Executions per second
slice	11391318.0 Ops/sec
for	118431.1 Ops/sec

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

LLMs can make mistakes. Check important info.

Let's dive into the world of MeasureThat.net, where we analyze JavaScript microbenchmarks.

**What is being tested?**

The provided benchmark tests two approaches to iterating over an array: using `slice` and using a traditional `for` loop.

In the first test case, "slice", the code `data.slice(150, 250)` is executed. This method creates a new array containing elements from index 150 (inclusive) to 250 (exclusive).

The second test case, "for", uses a traditional `for` loop to iterate over the array: `let dataFor = []; for (let i = 150; i < 250; i++) { dataFor.push(data[i]); }`. This approach manually increments a counter variable `i` and pushes elements from the original array into a new array `dataFor`.

**Options compared**

The benchmark compares two approaches:

1. **Slice**: Using the `slice` method to create a new array.
2. **For loop**: Using a traditional `for` loop to iterate over the array.

**Pros and cons of each approach:**

* **Slice**:
	+ Pros:
		- More concise and readable code
		- Creates a new, contiguous subarray with the desired range
	+ Cons:
		- May create an unnecessary copy of the original array, potentially leading to memory allocation overhead
		- Can be slower for very large arrays due to the creation of a new array
* **For loop**:
	+ Pros:
		- Direct access to individual elements in the array
		- No overhead of creating a new array
	+ Cons:
		- More verbose and less readable code
		- May be slower for very large arrays due to the overhead of manually incrementing indices

**Other considerations:**

* **Memory allocation**: When using `slice`, a new array is created, which can lead to memory allocation overhead. In contrast, the `for` loop approach avoids this overhead by only accessing existing elements in the original array.
* **Cache locality**: The `slice` method creates a contiguous subarray, which can improve cache locality and potentially speed up execution. However, for very large arrays, this benefit may be outweighed by the memory allocation overhead.

**Libraries used:**

None of the test cases use any external libraries. The code is self-contained and only uses built-in JavaScript features.

**Special JS features or syntax:**

The `slice` method is a built-in JavaScript array method that was introduced in ECMAScript 5 (ES5). It's widely supported by modern browsers and engines.

No special features or syntax are used in either test case. The code is straightforward and easy to understand.

**Alternatives:**

If you want to optimize the performance of your `slice`-based loop, you could consider using:

* **Array.prototype.slice.call()**: This method creates a new array from an existing array-like object.
* **Array.prototype.subarray()**: This method creates a new array containing elements from a specified range in the original array.

However, these alternatives may not provide significant performance benefits for typical use cases. The trade-offs between readability, memory allocation overhead, and cache locality should be carefully considered when choosing an approach.

Related benchmarks:

slice vs. for (version: 0)

Comparing performance of: slice vs for

Created: one year ago by: Registered User

Jump to the latest result

slice

for

Suite status: <idle, ready to run>

Experimental features:

Fastest: N/A

Slowest: N/A

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