Benchmark: sum calculation: forEach vs for vs forof vs reduce 2

Script Preparation code:

var arr = new Array(1000000).fill(0).map((x, i) => i + 10);
function sumFn(acc, x) { return acc + x};

Tests:

for of
let sum = 0; for (let x of arr) sum += x;
forEach
let sum = 0; arr.forEach(x => { sum += x })
for
let sum = 0; for(let i = 0; i < arr.length; i++) sum += arr[i];
reduce
const sum = arr.reduce(sumFn, 0)

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
for of
forEach
for
reduce

Fastest: N/A

Slowest: N/A

Latest run results:

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

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

The benchmark is comparing the performance of four different approaches to calculate the sum of an array:

1. `forEach`
2. `for` loop
3. `for...of` loop
4. `reduce()` method

**Options Comparison**

Here's a brief overview of each approach and their pros and cons:

* **forEach**: This method calls a provided callback function once for each element in the array, passing the current element as an argument. It's a good choice when you need to perform some operation on each element, but it doesn't provide any additional benefits like summing up all elements.
	+ Pros: easy to use, doesn't require manual indexing
	+ Cons: creates an extra loop and can be slower due to the callback function overhead
* **for** loop: This is a traditional loop that iterates over the array using manual indexing. It's a good choice when you need fine-grained control over the iteration process.
	+ Pros: provides direct access to each element, easy to optimize
	+ Cons: requires manual indexing, can be verbose and error-prone
* **for...of** loop: This is a more modern loop that allows iterating over arrays without manual indexing. It's similar to `forEach`, but with a few key differences.
	+ Pros: easy to use, doesn't require manual indexing
	+ Cons: slower due to the loop overhead
* **reduce()**: This method applies a provided callback function to each element in the array, accumulating a result. It's a good choice when you need to perform some operation that reduces the array to a single value.
	+ Pros: provides a concise way to sum up all elements, easy to use
	+ Cons: can be slower due to the callback function overhead

**Other Considerations**

* The `forEach` and `for...of` loops are similar in terms of performance, as they both iterate over the array without manual indexing. However, the `for...of` loop might have a slight edge due to its native support in modern browsers.
* The `for` loop is likely to be the slowest option due to the need for manual indexing and potential optimization overhead.

**Library and Special JS Features**

In this benchmark, there are no libraries used explicitly. However, it's worth noting that some of these approaches might rely on underlying library or framework optimizations (e.g., `reduce()`).

No special JS features are mentioned in this benchmark.

**Alternatives**

If you're interested in exploring alternative approaches for summing up an array, here are a few options:

* Use the `Array.prototype.every()` method instead of `forEach`, which can provide similar performance benefits.
* Consider using a parallel processing approach, such as Web Workers or async/await, to take advantage of multi-core processors and potentially speed up the calculation.
* Look into using specialized libraries like NumJS or Fast.js for optimized numerical computations.

Keep in mind that these alternatives might require significant changes to your codebase and may not provide a direct performance benefit compared to the original `forEach`, `for`, `for...of`, and `reduce()` approaches.

Related benchmarks:

sum calculation: forEach vs for vs forof vs reduce 2 (version: 1)

Comparing performance of: for of vs forEach vs for vs reduce

Created: 6 years ago by: Registered User

Jump to the latest result

for of

forEach

for

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):