Benchmark: toFixed vs toPrecision vs Math.round() - Parsed back to number

Script Preparation code:

var someFloat = 0.123456789;

Tests:

toFixed
parseFloat(someFloat.toFixed(4));
toPrecision
parseFloat(someFloat.toPrecision(4));
Math.round
(Math.round(someFloat*10000)/10000);

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
toFixed
toPrecision
Math.round

Fastest: N/A

Slowest: N/A

Latest run results:

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

User agent: Mozilla/5.0 (iPhone; CPU iPhone OS 15_2 like Mac OS X) AppleWebKit/605.1.15 (KHTML, like Gecko) Version/15.2 Mobile/15E148 Safari/604.1

Browser/OS: Mobile Safari 15 on iOS 15.2

View result in a separate tab

Test name	Executions per second
toFixed	4481461.5 Ops/sec
toPrecision	4190203.0 Ops/sec
Math.round	7738013.0 Ops/sec

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

LLMs can make mistakes. Check important info.

Let's break down the provided benchmark and explain what's being tested.

**Benchmark Overview**

The benchmark is designed to compare the performance of three different approaches to convert a floating-point number back to an integer:

1. `toFixed()`
2. `toPrecision()`
3. `Math.round()`

These functions are all part of the JavaScript language, and their behavior may vary depending on the browser or device being used.

**Options Compared**

The benchmark compares the performance of these three approaches in different browsers and devices. The options compared are:

* `toFixed(4)`: This method rounds the float to 4 decimal places using the "fixed" rounding mode.
* `toPrecision(4)`: This method uses the "fixed-point notation" with a precision of 4 digits, effectively rounding the float to 4 significant figures.
* `(Math.round(someFloat*10000)/10000)`: This approach multiplies the float by 10,000, rounds to the nearest integer using `Math.round()`, and then divides by 10,000.

**Pros and Cons of Each Approach**

Here's a brief summary of each approach:

* `toFixed(4)`:
	+ Pros: Simple, widely supported, and easy to understand.
	+ Cons: May not be suitable for all use cases (e.g., financial calculations), as the rounding mode can affect results.
* `toPrecision(4)`:
	+ Pros: More precise than `toFixed()` and can handle a wider range of input values. However, it may not be as widely supported as `toFixed()`.
	+ Cons: Can be less intuitive for developers unfamiliar with fixed-point notation.
* `(Math.round(someFloat*10000)/10000)`:
	+ Pros: More control over the rounding process and can handle large numbers.
	+ Cons: Requires more code and may be slower due to the multiplication step.

**Library Usage**

In this benchmark, none of the libraries are explicitly mentioned. However, it's worth noting that some browsers (e.g., Google Chrome) have a built-in `Number.EPSILON` constant that can affect the rounding behavior of these functions.

**Special JS Features/Syntax**

The benchmark doesn't use any special JavaScript features or syntax beyond what's provided in the standard library.

**Alternative Approaches**

Here are some alternative approaches to converting floating-point numbers back to integers:

* Using a decimal arithmetic library (e.g., Decimal.js) for more precise calculations.
* Employing a rounding algorithm like the Banker's Rounding method, which is more robust than the built-in rounding modes.
* Utilizing a third-party library (e.g., Rounding.js) that provides more advanced rounding capabilities.

These alternative approaches might offer better performance or accuracy in specific use cases, but they may also introduce additional complexity and dependencies.

Related benchmarks:

toFixed vs toPrecision vs Math.round() - Parsed back to number (version: 0)

Comparing performance of: toFixed vs toPrecision vs Math.round

Created: 2 years ago by: Guest

Jump to the latest result

toFixed

toPrecision

Math.round

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):