Benchmark: Distance Calc, pow vs mult 2

Script Preparation code:

var point1 = {x:5,y:10};
var point2 = {x:20,y:15};

Tests:

MULT
var x = (point1.x-point2.x)*(point1.x-point2.x)+(point1.y-point2.y)*(point1.y-point2.y);
POW
var y = Math.pow(point1.x-point2.x,2)+Math.pow(point1.y-point2.y,2);
MULT +
let d1 = point1.x-point2.x; let d2 = point1.y-point2.y; var x2 = d1*d1+d2*d2;
POW +
let d1 = point1.x-point2.x; let d2 = point1.y-point2.y; var y2 = Math.pow(d1,2)+Math.pow(d2,2);

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
MULT
POW
MULT +
POW +

Fastest: N/A

Slowest: N/A

Latest run results:

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

LLMs can make mistakes. Check important info.

Let's break down the provided benchmark and explain what is being tested, compared, and their pros and cons.

**Benchmark Definition**

The benchmark definition represents a mathematical expression that calculates the distance between two points (point1 and point2) using different approaches. The expression is:

`var x = (point1.x-point2.x)*(point1.x-point2.x)+(point1.y-point2.y)*(point1.y-point2.y);`

This is equivalent to calculating the Euclidean distance between two points.

**Comparison Options**

The benchmark compares four different approaches to calculate the Euclidean distance:

1. **`var x = (point1.x-point2.x)*(point1.x-point2.x)+(point1.y-point2.y)*(point1.y-point2.y);`** - This is the original expression in the benchmark definition.
2. **`var y = Math.pow(point1.x-point2.x,2)+Math.pow(point1.y-point2.y,2);`** - This approach uses the `Math.pow()` function to calculate the square of each difference and then adds them together.
3. **`let d1 = point1.x-point2.x;let d2 = point1.y-point2.y;var x2 = d1*d1+d2*d2;`** - This approach calculates the square of each difference separately (using `d1` and `d2`) and then adds them together.
4. **`let d1 = point1.x-point2.x;let d2 = point1.y-point2.y;var y2 = Math.pow(d1,2)+Math.pow(d2,2);`** - This approach calculates the square of each difference separately (using `d1` and `d2`) and then uses `Math.pow()` to calculate the square root.

**Pros and Cons**

Here's a brief summary of each approach:

* **Original expression**: Simple and straightforward, but may be less efficient due to repeated calculations.
* **`Math.pow()`**: Uses built-in exponentiation function, which is likely optimized for performance. However, it may not be as readable or maintainable as the original expression.
* **Separate square calculations**: More efficient than the original expression, as it avoids repeated calculations. However, it uses more variables and may be less readable.
* **`Math.pow()` with separate squares**: Combines the efficiency of `Math.pow()` with the simplicity of separate square calculations.

**Library Usage**

None of the approaches use any external libraries or frameworks beyond JavaScript's built-in functions (`Math.pow()`).

**Special JS Feature/Syntax**

There are no special JavaScript features or syntax used in this benchmark. The code is written in standard JavaScript syntax.

**Other Alternatives**

If you wanted to optimize the Euclidean distance calculation, you might consider using:

* **Binary GCD (Greatest Common Divisor)**: Instead of calculating the square root separately, you could use binary GCD to find the greatest common divisor of two numbers, which can be used to calculate the distance.
* **Sqrt caching**: If you know that the square root will be used multiple times in your code, you could cache it to avoid repeated calculations.

Keep in mind that these optimizations might not be necessary for this specific benchmark, and the comparison between the different approaches is already a good starting point.

Related benchmarks:

Distance Calc, pow vs mult 2 (version: 0)

Comparing performance of: MULT vs POW vs MULT + vs POW +

Created: 7 years ago by: Guest

Jump to the latest result

MULT

POW

MULT +

POW +

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):