Benchmark: normalize vector - Math.sqrt vs all squared

Tests:

Math.sqrt
const x = 1; const y = -1; const velocity = { x: 0, y: 0 }; const length = Math.sqrt(x * x + y * y); velocity.x = length > 0 ? x / length : 0; velocity.y = length > 0 ? y / length : 0;
all squared
const x = 1; const y = -1; const velocity = { x: 0, y: 0 }; const lengthSquared = x ** 2 + y ** 2; velocity.x = (lengthSquared > 0 ? x ** 2 / lengthSquared : 0) * (x < 0 ? -1 : 1); velocity.y = (lengthSquared > 0 ? y ** 2 / lengthSquared : 0) * (y < 0 ? -1 : 1);

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
Math.sqrt
all squared

Fastest: N/A

Slowest: N/A

Latest run results:

Run details: (Test run date: 2 years ago)

User agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/121.0.0.0 Safari/537.36

Browser/OS: Chrome 121 on Windows

View result in a separate tab

Test name	Executions per second
Math.sqrt	10621009.0 Ops/sec
all squared	134072440.0 Ops/sec

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

LLMs can make mistakes. Check important info.

Let's dive into the world of JavaScript microbenchmarks on MeasureThat.net.

**Benchmark Definition**

The benchmark definition is a JSON object that outlines the test case. In this case, there are two test cases:

1. `normalize vector - Math.sqrt vs all squared`: This test case measures the performance difference between using `Math.sqrt` and "all squared" (i.e., squaring the numbers) to calculate the length of a vector in 2D space.
2. `const x = 1; const y = -1; const velocity = { x: 0, y: 0 }; const lengthSquared = x ** 2 + y ** 2; velocity.x = (lengthSquared > 0 ? x ** 2 / lengthSquared : 0) * (x < 0 ? -1 : 1); velocity.y = (lengthSquared > 0 ? y ** 2 / lengthSquared : 0) * (y < 0 ? -1 : 1);` is the "all squared" approach.

**Options Compared**

The two test cases compare the performance of:

* `Math.sqrt`: A built-in JavaScript function that calculates the square root of a number.
* "All squared": An alternative method that squares the numbers and then performs the division to calculate the length.

**Pros and Cons**

Here's a brief summary of the pros and cons of each approach:

* **`Math.sqrt`**:
	+ Pros: More readable, easier to understand, and potentially more efficient for small numbers.
	+ Cons: May be slower for larger numbers due to its internal implementation.
* "All squared":
	+ Pros: Can be faster for very large numbers or when dealing with floating-point precision issues.
	+ Cons: Less readable and may lead to numerical instability if not implemented correctly.

**Library**

There is no specific library used in this benchmark. The test cases rely on built-in JavaScript functions, such as `Math.sqrt`.

**Special JS Features or Syntax**

None of the test cases use any special JavaScript features or syntax beyond what's considered standard by MeasureThat.net. No polyfills or transpilers are required to run these tests.

**Other Alternatives**

If you're interested in exploring alternative approaches, here are a few options:

* Using `Math.hypot` (JavaScript 1.8+): This function calculates the Euclidean distance between two points and might be faster than using `Math.sqrt`.
* Implementing a custom square root calculation using bit manipulation or arithmetic reduction: These methods can provide better performance for large numbers, but are less readable and more complex to implement.

Keep in mind that benchmarking performance is highly dependent on specific use cases and hardware configurations. MeasureThat.net provides a useful platform for comparing different approaches, but you should always consider your own specific requirements and constraints when choosing a method.

Related benchmarks:

normalize vector - Math.sqrt vs all squared (version: 0)

Comparing performance of: Math.sqrt vs all squared

Created: 2 years ago by: Guest

Jump to the latest result

Math.sqrt

all squared

Suite status: <idle, ready to run>

Fastest: N/A

Slowest: N/A

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