Benchmark: Test for - MeasureThat.net

Script Preparation code:

var arr = [];
  for (var i = 0; i < 1000; i++) {
    arr[i] = i;
  }

  function someFn(ix) {
    return ix * 5;
  }

Tests:

while
var l = arr.length; while(l--) { someFn(arr[l]); }
for
for (var i = 0; i < arr.length; i++) { someFn(arr[i]); }

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
while
for

Fastest: N/A

Slowest: N/A

Latest run results:

Run details: (Test run date: 7 years ago)

User agent: Mozilla/5.0 (Macintosh; Intel Mac OS X 10_12_1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/56.0.2924.87 Safari/537.36

Browser/OS: Chrome 56 on Mac OS X 10.12.1

View result in a separate tab

Test name	Executions per second
while	7140.1 Ops/sec
for	4729.7 Ops/sec

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

LLMs can make mistakes. Check important info.

Let's break down what's happening in this benchmark and explain the concepts involved.

**Benchmark Definition**
The provided JSON represents a JavaScript microbenchmark. It consists of two main parts:

1. **Script Preparation Code**: This code is executed once, before running any tests. In this case, it creates an array `arr` with 1000 elements and defines a function `someFn(ix)` that takes an index `ix` and returns the product of `ix` and 5.
2. **Html Preparation Code**: This field is empty in this benchmark, which means no HTML code is executed before running any tests.

**Individual Test Cases**
There are two test cases:

1. **Test Case: while**
The benchmark definition for this test case contains a while loop that iterates over the array `arr` using its length as the counter variable. Inside the loop, it calls the `someFn(arr[l])` function, where `l` is the current index.
2. **Test Case: for**
Similarly, this test case uses a traditional `for` loop to iterate over the elements of the array `arr`. In this case, the loop variable `i` takes on values from 0 to 999 (the length of the array), and it calls the same `someFn(arr[i])` function.

**Options Being Compared**
In this benchmark, two options are being compared:

1. **While Loop**: The while loop is executed using its own counter variable (`l`) instead of relying on the length of the array.
2. **For Loop**: The traditional `for` loop uses a separate counter variable (`i`) and relies on the length of the array to control the iteration.

**Pros and Cons**

* **While Loop**:
	+ Pros: Can potentially be faster because it avoids an extra lookup to get the length of the array.
	+ Cons: May not work as expected if the array is modified while iterating, since `l` will become outdated. Also, this approach can lead to unexpected behavior if the loop condition changes.
* **For Loop**:
	+ Pros: More predictable and safe, especially when working with large datasets or complex logic. It also avoids potential issues with modifying the array during iteration.
	+ Cons: Requires an extra lookup to get the length of the array, which can introduce overhead.

**Library Used**
There is no library explicitly mentioned in the benchmark definition or test cases. However, it's likely that a library like `Benchmark.js` or `Microbenchmark` was used to create and run these tests.

**Special JS Feature/Syntax**
There are no special JavaScript features or syntax mentioned in this benchmark. It uses standard language constructs for both loops (while and for).

**Other Alternatives**

If you're interested in optimizing your own microbenchmarks, consider using libraries like:

1. `Benchmark.js`: A popular benchmarking library that provides a simple API for creating and running benchmarks.
2. `Microbenchmark`: A lightweight library that allows you to write custom benchmarks with minimal overhead.

When creating or optimizing your own benchmarks, keep in mind the trade-offs between predictability, safety, and performance.

Related benchmarks:

Test for (version: 0)

Comparing performance of: while vs for

Created: 7 years ago by: Guest

Jump to the latest result

while

for

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):