Benchmark: Euclidean distance comparison

Script Preparation code:

pointA = { x: 1, y: 2};
pointB = { x: 10, y: 5};
maxDistance = 4;

Tests:

Simple
Math.sqrt(Math.pow(pointA.x - pointB.x, 2) + Math.pow(pointA.y - pointB.y, 2)) < maxDistance
Squared comparator
(Math.pow(pointA.x - pointB.x, 2) + Math.pow(pointA.y - pointB.y, 2)) < Math.pow(maxDistance, 2)

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
Simple
Squared comparator

Fastest: N/A

Slowest: N/A

Autogenerated LLM Summary (model llama3.2:3b, generated 25 days ago):

LLMs can make mistakes. Check important info.

I'd be happy to explain what's being tested in the provided benchmark.

**Overview**

The benchmark compares two approaches to calculating the Euclidean distance between two points: `Math.sqrt(Math.pow(pointA.x - pointB.x, 2) + Math.pow(pointA.y - pointB.y, 2)) < maxDistance` and `(Math.pow(pointA.x - pointB.x, 2) + Math.pow(pointA.y - pointB.y, 2)) < Math.pow(maxDistance, 2)`.

**Approach 1: Simple**

This approach uses the traditional mathematical formula for Euclidean distance:

√((x2 - x1)^2 + (y2 - y1)^2)

In this case, it's used in conjunction with a less-than comparison operator (`<`) to check if the calculated distance is less than a predefined maximum distance.

**Pros and Cons**

Pros:

* Easy to understand and implement
* Uses standard mathematical library functions

Cons:

* May lead to slower performance due to floating-point arithmetic and comparison operations
* Can be more prone to rounding errors

**Approach 2: Squared comparator**

This approach squares both the calculated Euclidean distance and the maximum distance before comparing them. This simplifies the calculation and avoids dealing with square roots.

`Math.pow(pointA.x - pointB.x, 2) + Math.pow(pointA.y - pointB.y, 2) < Math.pow(maxDistance, 2)`

**Pros and Cons**

Pros:

* Faster performance due to fewer operations
* Less prone to rounding errors

Cons:

* Can be less intuitive for developers who are not familiar with this approach
* May require more careful consideration of the maximum distance value to ensure accurate results

**Library usage**

The benchmark uses no external libraries, relying solely on JavaScript's built-in `Math` object.

**Special JS feature/syntax**

There is no special JavaScript feature or syntax being used in this benchmark. The code appears to be written in standard JavaScript.

**Other alternatives**

In theory, other approaches could be taken:

1. Using a library like LRU Cache to memoize the results of expensive calculations.
2. Utilizing a Just-In-Time (JIT) compiler to optimize the performance of the calculation.
3. Applying parallel processing techniques to calculate both distances simultaneously.
4. Leveraging a GPU-accelerated JavaScript engine to offload computationally intensive tasks.

However, these alternatives are not shown in this benchmark definition and may require significant modifications to the code.

I hope this explanation helps! Let me know if you have any further questions or need clarification on any of these points.

Latest run results:

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

User agent: Mozilla/5.0 (X11; Ubuntu; Linux x86_64; rv:109.0) Gecko/20100101 Firefox/113.0

Browser/OS: Firefox 113 on Ubuntu

View result in a separate tab

Test name	Executions per second
Simple	1457070208.0 Ops/sec
Squared comparator	1669224704.0 Ops/sec

Related benchmarks:

Euclidean distance comparison (version: 0)

A demonstration of efficient and inefficient eculidean distance comparisons

Comparing performance of: Simple vs Squared comparator

Created: one year ago by: Guest

Jump to the latest result

Simple

Squared comparator

Suite status: <idle, ready to run>

Experimental features:

Fastest: N/A

Slowest: N/A

Autogenerated LLM Summary (model llama3.2:3b, generated 25 days ago):