Benchmark: Math.sign speed comparison

Script Preparation code:

var x = Math.random()
var sign = Math.sign

Tests:

Math.abs access
Math.sign(x)
abs function
sign(x)
compare
x < 0 ? -1 : x > 0 ? 1 : 0

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
Math.abs access
abs function
compare

Fastest: N/A

Slowest: N/A

Latest run results:

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

User agent: Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/605.1.15 (KHTML, like Gecko) Version/17.3.1 Safari/605.1.15

Browser/OS: Safari 17 on Mac OS X 10.15.7

View result in a separate tab

Test name	Executions per second
Math.abs access	5151164.5 Ops/sec
abs function	6461668.5 Ops/sec
compare	6785627.5 Ops/sec

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.

**Benchmark Purpose**

The benchmark is designed to compare the performance of three different approaches for calculating the absolute value (or sign) of a number in JavaScript. The three approaches are:

1. Directly accessing the `abs` function from the Math library (`Math.abs(x)`).
2. Accessing the `sign` function from the Math library (`Math.sign(x)`).
3. Using a conditional expression to determine the sign of a number based on its value (`x < 0 ? -1 : x > 0 ? 1 : 0`).

**Options Comparison**

The benchmark is comparing the performance of these three approaches, with each approach being executed multiple times to generate the final execution count. The goal is to determine which approach is the fastest.

Pros and Cons of Each Approach:

* **Directly accessing `Math.abs(x)`**: This approach is straightforward and easy to understand. However, it may not be optimized for performance, as it involves a function call.
	+ Pros: Easy to implement and understand.
	+ Cons: May not be optimized for performance.
* **Accessing `Math.sign(x)`**: This approach is similar to the first one but uses a different method to calculate the sign. It's also not optimized for performance.
	+ Pros: Similar ease of implementation as the first approach.
	+ Cons: Not optimized for performance.
* **Conditional expression**: This approach uses a ternary operator to determine the sign based on the value of `x`. While it may be more concise, it can also be less readable and maintainable.
	+ Pros: More concise and potentially easier to understand than the first two approaches.
	+ Cons: May be less readable and maintainable.

**Library Used**

The benchmark uses the Math library, which provides several utility functions for mathematical operations. The `abs` function is used in the first approach, while the `sign` function is used in the second approach.

**Special JS Feature or Syntax**

There doesn't appear to be any special JavaScript feature or syntax being tested in this benchmark. However, it's worth noting that the use of the ternary operator (`x < 0 ? -1 : x > 0 ? 1 : 0`) is an example of a concise and expressive way to write conditional logic.

**Other Alternatives**

If you're interested in exploring alternative approaches for calculating absolute values or signs, here are some other options:

* Using the `Math.abs` function with a bitwise AND operator (`x & 0xffffffff`)
* Implementing your own `abs` function using bit manipulation
* Using a library like Lodash's `abs` function

Keep in mind that these alternatives may have different performance characteristics or trade-offs in terms of readability and maintainability.

Overall, the benchmark provides a useful insight into the performance characteristics of different approaches for calculating absolute values or signs in JavaScript.

Related benchmarks:

Math.sign speed comparison (version: 1)

Comparing performance of: Math.abs access vs abs function vs compare

Created: 3 years ago by: Registered User

Jump to the latest result

Math.abs access

abs function

compare

Suite status: <idle, ready to run>

Fastest: N/A

Slowest: N/A

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