Benchmark: Array.reduce vs for loop vs Array.forEach experiments

Script Preparation code:

// Create an array of 1000 random intergers between 1 and 10000
var arrRandom = [];
for(var intCtr=0; intCtr<1000; intCtr++) {
  arrRandom.push(Math.floor(Math.random() * Math.floor(10000)));
}

function doRedeuce(pArray, start) {
	return pArray.reduce(function(accum, curr) { return accum + curr; }, start);
}
function doRedeuceArrowFunc(pArray, start) {
	return pArray.reduce((accum, curr) => accum + curr, start);
}
function doLoop(pArray) {
	var accum = 0;
	for(var intCtr=0; intCtr<pArray.length; intCtr++) {
		accum += pArray[intCtr];
	}
	return accum;
}
function doForEach(pArray) {
	var accum = 0;
	pArray.forEach((item) => {
		accum += item;
	});
}

Tests:

reduce (arrow function)
var reduceResult=0; redeuceResult = doRedeuceArrowFunc(arrRandom, 0);
for loop
var loopResult=0; loopResult = doLoop(arrRandom);
forEach
var forEachResult=0 forEachResult = doForEach(arrRandom)
reduce
var reduceResult=0; redeuceResult = doRedeuce(arrRandom, 0);

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
reduce (arrow function)
for loop
forEach
reduce

Fastest: N/A

Slowest: N/A

Latest run results:

No previous run results

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

LLMs can make mistakes. Check important info.

Let's dive into the world of JavaScript microbenchmarks.

**Benchmark Overview**

The provided benchmark compares three different approaches to sum an array of 1000 random integers: `Array.reduce`, `for` loop, and `Array.forEach`. The goal is to determine which approach is the fastest.

**Options Compared**

1. **Array.reduce**: This method uses a callback function to reduce the array to a single value, accumulating the sum.
2. **For Loop**: A traditional `for` loop is used to iterate over the array, adding each element to a running total.
3. **Array.forEach**: The `forEach` method is used to execute an action on each element in the array, in this case, adding the element to a running total.

**Pros and Cons**

* **Array.reduce**:
	+ Pros: Concise, expressive, and easy to read.
	+ Cons: Can be slower than traditional loops due to the overhead of function calls.
* **For Loop**:
	+ Pros: Fast, control over iteration order, and no function call overhead.
	+ Cons: Verbose, harder to read, and more error-prone.
* **Array.forEach**:
	+ Pros: Concise, expressive, and easy to read.
	+ Cons: Slower than traditional loops due to the overhead of function calls.

**Library/Function Used**

In this benchmark, `Math.floor` is used to generate random integers between 1 and 10000. No external libraries are required.

**Special JS Feature/Syntax**

None mentioned in the provided code. However, it's worth noting that modern JavaScript supports arrow functions (`=>`) which can simplify code and improve readability.

**Other Alternatives**

If you wanted to include additional approaches, some alternatives could be:

* `Array.prototype.reduceRight`
* Using a library like Lodash for more concise implementation
* Using SIMD instructions (e.g., with WebAssembly) for parallel processing

Keep in mind that these alternatives might not be directly applicable to this specific benchmark.

**Benchmark Preparation Code**

The provided script preparation code creates an array of 1000 random integers between 1 and 10000, defines three functions (`doRedeuce`, `doRedeuceArrowFunc`, and `doLoop`) for each approach, and initializes variables for the results. The HTML preparation code is empty in this case.

**Individual Test Cases**

Each test case executes one of the three approaches (reduce, for loop, or forEach) on the generated array, accumulating the sum in a variable.

Related benchmarks:

Array.reduce vs for loop vs Array.forEach experiments (version: 3)

A test summing 1000 random numbers, 1 - 10000

Comparing performance of: reduce (arrow function) vs for loop vs forEach vs reduce

Created: 7 years ago by: Registered User

Jump to the latest result

reduce (arrow function)

for loop

forEach

reduce

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):