Benchmark: i hate l00ps with optimaze for and with function

Script Preparation code:

var numbers = Array.from({ length: 10_000 }).map(() => Math.random())

Tests:

functional
const result = numbers .map(n => Math.round(n * 10)) .filter(n => n % 2 === 0) .reduce((a, n) => a + n, 0)
imperative
let result = 0 for (let i = 0,len =numbers.length; i <len ; i++) { let n = Math.round(numbers[i] * 10) if (n % 2 !== 0) continue result = result + n }
functional but better
const round = n => Math.round(n * 10) const isEven = n => n % 2 === 0 const result = numbers .reduce((a, n) => { n = round(n) return isEven(n) ? a + n : a; }, 0)
imperative with function
const round = n => Math.round(n * 10) const isEven = n => n % 2 === 0 let result = 0 for (let i = 0,len =numbers.length; i <len ; i++) { let n = round(numbers[i]) if (!isEven(n)) continue result = result + n }

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
functional
imperative
functional but better
imperative with function

Fastest: N/A

Slowest: N/A

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

LLMs can make mistakes. Check important info.

Let's dive into the world of JavaScript microbenchmarks on MeasureThat.net.

**Benchmark Overview**

The provided benchmark definition represents two types of loops: Imperative and Functional. The Script Preparation Code generates an array of 10,000 random numbers, which is then used to test various loop implementations.

**Loop Types**

There are three main loop types compared:

1. **Imperative**: A traditional loop that uses a counter variable `i` to iterate over the array.
2. **Functional**: A loop that uses JavaScript's built-in `map()`, `filter()`, and `reduce()` methods to process the data.
3. **Imperative with Function**: A hybrid approach that combines an imperative loop with function calls.

**Options Compared**

The three options are compared in terms of their performance, readability, and maintainability.

* **Imperative**: This option is a straightforward, traditional loop that uses a counter variable `i` to iterate over the array. It's easy to understand but might not be the most efficient.
* **Functional**: This option uses JavaScript's built-in functions to process the data, which can lead to more concise and readable code. However, it might require more memory allocation and garbage collection due to the creation of intermediate arrays.
* **Imperative with Function**: This hybrid approach combines an imperative loop with function calls. It allows for some function abstraction, but still uses a traditional loop.

**Pros and Cons**

Here's a brief summary of each option:

* **Imperative**:
	+ Pros: Easy to understand, low memory allocation.
	+ Cons: Might be less efficient due to the need to update the counter variable `i`.
* **Functional**:
	+ Pros: More concise and readable code, leverages JavaScript's built-in functions.
	+ Cons: Might require more memory allocation and garbage collection due to intermediate arrays.
* **Imperative with Function**:
	+ Pros: Balances readability with some function abstraction, avoids the need for explicit counter variable updates.
	+ Cons: Still uses a traditional loop, might not be as concise as the functional approach.

**Library Used**

There is no library explicitly mentioned in the benchmark definition. However, it's worth noting that JavaScript's built-in functions like `map()`, `filter()`, and `reduce()` are part of the language itself and don't require any external libraries to use.

**Special JS Feature or Syntax**

The benchmark doesn't explicitly mention any special JavaScript features or syntax beyond the standard ones. However, it does utilize JavaScript's array methods (`map()`, `filter()`, and `reduce()`), which might be considered a "feature" in itself.

**Alternatives**

Some possible alternatives to these loop types could include:

* Using a for...of loop instead of a traditional for loop.
* Leveraging parallel processing or concurrency techniques using Web Workers, async/await, or other libraries.
* Employing more advanced algorithms or data structures, like heaps or queues, to optimize the loop.

Keep in mind that these alternatives might not be directly relevant to the specific benchmark and might require additional context or modifications to the script preparation code.

Latest run results:

Run details: (Test run date: 8 months ago)

User agent: Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/122.0.0.0 Safari/537.36

Browser/OS: Chrome 122 on Linux

View result in a separate tab

Test name	Executions per second
functional	1361.8 Ops/sec
imperative	1112.9 Ops/sec
functional but better	1533.8 Ops/sec
imperative with function	1172.5 Ops/sec

Related benchmarks:

i hate l00ps with optimaze for and with function (version: 0)

Comparing performance of: functional vs imperative vs functional but better vs imperative with function

Created: 8 months ago by: Guest

Jump to the latest result

functional

imperative

functional but better

imperative with function

Suite status: <idle, ready to run>

Experimental features:

Fastest: N/A

Slowest: N/A

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