Benchmark: Array.reduce vs for loop vs Array.forEachasdfasdfas

Script Preparation code:

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

Tests:

reduce
var redeuceResult=0; redeuceResult = doRedeuce(arrRandom);
for loop
var loopResult=0; loopResult = doLoop(arrRandom);
forEach
var forEachResult=0 forEachResult = doForEach(arrRandom)

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
reduce
for loop
forEach

Fastest: N/A

Slowest: N/A

Autogenerated LLM Summary (model llama3.2:3b, generated 27 days ago):

LLMs can make mistakes. Check important info.

Let's break down what's being tested in this benchmark.

**Benchmark Definition:**

The benchmark is designed to compare the performance of three different approaches for summing an array of 10,000 random integers:

1. `Array.prototype.reduce()`: a callback-based method that iterates over the array and accumulates the sum.
2. A traditional `for` loop: manually iterating over the array using a counter variable.
3. `Array.prototype.forEach()`: a method that invokes a callback function for each element in the array.

**Comparison of options:**

* **`Array.prototype.reduce()`**: This approach uses a callback function to accumulate the sum. It's generally considered the most efficient and concise way to perform this operation, as it avoids explicit loops.
	+ Pros:
		- Concise code
		- Efficient, as it only requires a single pass over the array
		- Can be easily parallelized or optimized with caching
	+ Cons:
		- May have slower performance due to overhead of callback invocation and state management
* **Traditional `for` loop**: This approach uses an explicit loop to iterate over the array, adding each element to a running sum.
	+ Pros:
		- Fast, as it's a straightforward iteration without any additional function calls
		- Easy to understand and debug for developers familiar with traditional loops
	+ Cons:
		- Less concise than `reduce()`
		- Requires manual management of the loop counter and array indexing
* **`Array.prototype.forEach()`**: This approach uses an iterator callback function to perform an operation on each element in the array. However, in this specific benchmark, it's not using the iterator (as it's called "forEach" without the "iterable" suffix), so we'll assume it's simply iterating over the array.
	+ Pros:
		- Fast, as it only requires a single pass over the array
	+ Cons:
		- May have slower performance due to overhead of callback invocation and state management (similar to `reduce()`)

**Library:**

The benchmark uses `Array.prototype.reduce()`, which is a built-in method in JavaScript. The purpose of this method is to apply a function to each element in the array, accumulating a value.

**Special JS feature or syntax:**

None mentioned explicitly. However, it's worth noting that the `forEach()` method can be used with an iterator (e.g., `Array.prototype.forEach(iterable, callback)`) for more advanced use cases. In this benchmark, we assume it's simply iterating over the array.

**Other considerations:**

* **Cache locality**: The performance of each approach may vary depending on how well they utilize cache locality, which can affect memory access patterns.
* **Browser-specific optimizations**: Different browsers might optimize these functions or approaches differently, affecting benchmark results.

**Alternative approaches:**

1. `Array.prototype.map()` with callback-based summing
2. Using a specialized library for numerical computations (e.g., NumJS)
3. Implementing a custom iterator for `forEach()`
4. Using parallel processing or concurrency techniques to speed up the benchmark

Latest run results:

Run details: (Test run date: 4 years ago)

User agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/80.0.3987.149 Safari/537.36

Browser/OS: Chrome 80 on Windows

View result in a separate tab

Test name	Executions per second
reduce	2842.2 Ops/sec
for loop	74661.2 Ops/sec
forEach	4862.5 Ops/sec

Related benchmarks:

Array.reduce vs for loop vs Array.forEachasdfasdfas (version: 0)

A test summing 1000 random numbers, 1 - 10000

Comparing performance of: reduce vs for loop vs forEach

Created: 4 years ago by: Guest

Jump to the latest result

reduce

for loop

forEach

Suite status: <idle, ready to run>

Experimental features:

Fastest: N/A

Slowest: N/A

Autogenerated LLM Summary (model llama3.2:3b, generated 27 days ago):