Benchmark: array iteration Large - break

Script Preparation code:

var myArrayLarge = Array(10000);
for (let i=0; i<10000; i++) {
	myArrayLarge[i] = i; 
}
console.log("setup complete!");

Tests:

for element of LARGE
console.log("starting test 1"); let myArr = [] for (ele of myArrayLarge) { if (myArr.length > 300) { continue; } myArr.push(ele); } console.log("for element of LARGE: ", myArr.length);
for const element of LARGE
console.log("starting test 2"); let myArr = [] for (const ele of myArrayLarge) { if (myArr.length > 300) { continue; } myArr.push(ele); } console.log("for const element of LARGE: ", myArr.length, myArr);
myArr forEach LARGE
let myArr = [] myArrayLarge.forEach(function(s) { myArr.push(ele); if (myArr.length > 300) { return; } }); console.log("myArr forEach LARGE: ", myArr.length)
array index LARGE
let myArr = new Array(300) for (let i=0; i<300; i++) { let something = myArrayLarge[i]; myArr[i] = myArrayLarge[i]; } console.log("array index LARGE: ", myArr.length)
map LARGE
let myArr = [] myArrayLarge.map((i) => { if (myArr.length > 300) { continue; } myArr.push(i); }); console.log("map LARGE: ", myArr.length)
forEach ES6 LARGE
let myArr = [] myArrayLarge.forEach(s => { if (myArr.length > 300) { continue; } myArr.push(s); }); console.log("forEach ES6 LARGE: ", myArr.length)

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
for element of LARGE
for const element of LARGE
myArr forEach LARGE
array index LARGE
map LARGE
forEach ES6 LARGE

Fastest: N/A

Slowest: N/A

Latest run results:

Run details: (Test run date: 6 years ago)

User agent: Mozilla/5.0 (Windows NT 6.1; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/67.0.3396.87 Safari/537.36

Browser/OS: Chrome 67 on Windows 7

View result in a separate tab

Test name	Executions per second
for element of LARGE	3287.2 Ops/sec
for const element of LARGE	231170.2 Ops/sec
myArr forEach LARGE	11.5 Ops/sec
array index LARGE	12.1 Ops/sec
map LARGE	12.0 Ops/sec
forEach ES6 LARGE	12.2 Ops/sec

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

LLMs can make mistakes. Check important info.

Let's break down the provided benchmark JSON and explain what's being tested.

**Benchmark Definition**

The benchmark definition is a JavaScript code snippet that describes the test. In this case, it's an array iteration benchmark with a large array (`myArrayLarge`) containing 10,000 elements. The test checks how different ways of iterating over this array affect performance.

**Options compared**

There are four options being compared:

1. **For...of loop**: This is a modern JavaScript loop that allows you to iterate over arrays without the need for an index variable.
2. **Traditional For loop**: A classic loop that uses an index variable to access each element in the array.
3. **forEach() function**: An older method of iterating over arrays, which takes a callback function as an argument.
4. **Map() function**: Although not directly used for iteration, Map() is being used to create a new array (`myArr`) from `myArrayLarge`. We'll assume that the actual performance test involves using `forEach()` or other methods on this array.

**Pros and Cons**

Here's a brief overview of each option:

* **For...of loop**: Pros: concise, easy to read, efficient. Cons: not as widely supported as traditional loops.
* **Traditional For loop**: Pros: widely supported, flexible, but can be verbose. Cons: less efficient than modern loops like `for...of`.
* **forEach() function**: Pros: simple, readable, widely supported. Cons: not designed for performance-critical applications and can lead to slower execution.
* **Map() function** (used for array creation): Pros: concise, efficient, but requires careful consideration of performance implications when iterating over the resulting array.

**Other considerations**

When writing these benchmarks, it's essential to consider factors like:

* Cache locality: How does each iteration method affect cache usage and access patterns?
* Branch prediction: Does the iteration method lead to more or fewer branch mispredictions, which can impact performance?

**Library/Function mentioned**

In this benchmark, `Array.prototype.forEach()` is being used as part of the test. The purpose of `forEach()` here is to iterate over each element in the large array (`myArrayLarge`) and push elements to a new array (`myArr`).

**Special JS feature or syntax**

The main special feature used here is the modern JavaScript loop, specifically **For...of**, which allows iterating over arrays without an explicit index variable.

Now that we've explored the benchmark, let's discuss alternatives:

* For those interested in exploring more performance-oriented approaches:
	+ Consider using `Array.prototype.forEach()` with a custom iterator to improve cache locality and branch prediction.
	+ Look into other iteration methods like `Array.prototype.map()` or `for...in` loops, which might offer different performance characteristics depending on the specific use case.

For developers seeking alternative ways to approach this problem:

* If you're interested in exploring traditional loop approaches:
	+ Use a `for` loop with an index variable for direct access and control.
	+ Consider using `Array.prototype.forEach()` for a simpler, readable solution.
* For those looking into optimizing iteration performance:
	+ Experiment with different array creation methods (e.g., `Array.from()`, `Array.prototype.slice()`) to find the most efficient approach.
	+ Investigate other factors influencing performance, such as caching and branch prediction.

Related benchmarks:

array iteration Large - break (version: 4)

Comparing performance of: for element of LARGE vs for const element of LARGE vs myArr forEach LARGE vs array index LARGE vs map LARGE vs forEach ES6 LARGE

Created: 6 years ago by: Registered User

Jump to the latest result

for element of LARGE

for const element of LARGE

myArr forEach LARGE

array index LARGE

map LARGE

forEach ES6 LARGE

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):