Benchmark: reduce vs for loop

Script Preparation code:

var arrRandom = [];
for(var intCtr=0; intCtr<1000; intCtr++) {
  arrRandom.push(Math.floor(Math.random() * Math.floor(10000)));
}
function doRedeuce(pArray) {
	return pArray.reduce(function(accum, curr) {return accum+curr});
}
function doLoop(pArray) {
	var accum = 0;
	for(var intCtr=0; intCtr<pArray.length; intCtr++) {
		accum += pArray[intCtr];
	}
	return accum;
}

Tests:

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

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
reduce
for loop

Fastest: N/A

Slowest: N/A

Latest run results:

Run details: (Test run date: 2025 years ago)

User agent: Mozilla/5.0 (Macintosh; Intel Mac OS X 10_11_6) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/64.0.3282.140 Safari/537.36

Browser/OS: Chrome 64 on Mac OS X 10.11.6

View result in a separate tab

Test name	Executions per second
reduce	73620.5 Ops/sec
for loop	672650.7 Ops/sec

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

LLMs can make mistakes. Check important info.

I'd be happy to help you understand the provided JSON benchmark.

**What is tested?**

The provided JSON represents two JavaScript microbenchmarks: `reduce` and `for loop`. The benchmarks test which approach (reduction using `Array.prototype.reduce()` or manual iteration with a `for` loop) is faster for summing 1000 random numbers generated by the `arrRandom` array.

**Options compared**

The options being tested are:

1. **Reduction using `Array.prototype.reduce()`**: This method uses a callback function to iterate over the elements of the array and accumulate the result.
2. **Manual iteration with a `for` loop**: This approach involves manually iterating over each element of the array using a `for` loop, adding up the values as needed.

**Pros and Cons**

Here are some pros and cons of each approach:

1. **Reduction using `Array.prototype.reduce()`**:
	* Pros: More concise and readable code, eliminates the need for manual index management.
	* Cons: May have higher overhead due to function call and object lookup, potentially slower execution compared to a simple loop.
2. **Manual iteration with a `for` loop**:
	* Pros: Can be more efficient due to reduced function call and object lookup overhead, may be faster in some cases.
	* Cons: More verbose code, requires manual index management, which can lead to errors.

**Library**

In this benchmark, the `Array.prototype.reduce()` method is used. The `reduce()` function is a built-in JavaScript method that applies a callback function to each element of an array, accumulating a result.

**Special JS feature/syntax**

None are mentioned in the provided JSON.

**Other considerations**

When writing performance-critical code, it's essential to consider factors such as:

* Cache locality: Is the data being accessed sequentially or randomly? This can impact performance.
* Branch prediction: Are the loops predicting the direction of execution correctly?
* Overhead: What are the overheads associated with function calls, object lookups, and other operations?

**Alternatives**

Other alternatives to these approaches include:

1. **Using `Array.prototype.forEach()`**: Instead of `reduce()`, you can use `forEach()` with a callback function to iterate over the array.
2. **Using `for...of` loop**: Modern JavaScript introduces `for...of` loops, which provide a more concise way to iterate over arrays compared to traditional `for` loops.
3. **Using SIMD instructions**: If available on your target platform, you can use SIMD (Single Instruction, Multiple Data) instructions to perform operations on multiple elements simultaneously.

Keep in mind that the performance difference between these approaches may be negligible for small datasets like this one. However, as the dataset grows or becomes more complex, the performance differences become more significant.

Related benchmarks:

reduce vs for loop (version: 0)

A test summing 1000 random numbers

Comparing performance of: reduce vs for loop

Created: 6 years ago by: Guest

Jump to the latest result

reduce

for loop

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):