Benchmark: Number([num].toFixed(precision)) vs scaling + Math.round()

Script Preparation code:

var precision = 4;
var someFloat = 121251.123456789012;

Tests:

toFixed(4)
Number(someFloat.toFixed(precision));
Scale and round
var scale = 10 ** precision; Math.round(someFloat * scale) / scale;

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
toFixed(4)
Scale and round

Fastest: N/A

Slowest: N/A

Latest run results:

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

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I'll break down the benchmark and explain what's being tested, compared, and the pros and cons of each approach.

**Benchmark Overview**

The benchmark is comparing two approaches to convert a floating-point number to an integer:

1. `Number([num].toFixed(precision))`
2. `var scale = 10 ** precision; Math.round(someFloat * scale) / scale`

The goal is to determine which approach is faster and more efficient.

**Library Used**

In this benchmark, the `Math` library is used. Specifically, the `toFixed()` method, `Math.round()`, and exponentiation (`10 ** precision`) are utilized.

**Special JavaScript Feature/Syntax**

None are explicitly mentioned in this benchmark.

**Benchmark Definition Explanation**

The first benchmark definition uses `Number([num].toFixed(precision))`. This approach:

* Converts the floating-point number to a string using `toFixed(precision)`
* Uses `Number()` to convert the resulting string back to a number

This approach can lead to additional overhead due to the string conversion.

**Scale and Round Approach**

The second benchmark definition uses `var scale = 10 ** precision; Math.round(someFloat * scale) / scale`. This approach:

* Calculates an exponential scaling factor (`scale`) based on the desired precision
* Multiplies the original floating-point number by the scaled value using `Math.round()`
* Divides the result by the scaled value to recover the original integer

This approach is often referred to as "scaling and rounding" or "arbitrary-precision arithmetic."

**Pros and Cons**

**Scaling and Rounding Approach (Scale and round)**

Pros:

* More efficient than string conversion
* Can take advantage of arbitrary-precision arithmetic
* May be faster for larger values due to reduced overhead

Cons:

* Requires additional calculations (exponentiation, multiplication, division)
* May require more memory allocation due to the scaled value

**String Conversion Approach (toFixed)**

Pros:

* Simple and easy to understand
* Does not require additional memory allocation

Cons:

* Can lead to additional overhead due to string conversion
* May be slower for larger values due to increased complexity

**Other Alternatives**

Some alternative approaches that could be considered in a similar benchmark include:

1. `Number(someFloat.toExponential(precision))`: This approach uses the `toExponential()` method instead of `toFixed()`. While it might seem more efficient, it's less common and may not provide the same level of precision.
2. `someFloat * 10 ** precision / 10 ** precision`: This approach multiplies and divides by a power of 10 to achieve scaling, but it can lead to rounding errors.

Keep in mind that these alternatives might not be as well-established or widely supported, so they may not be suitable for production use.

Related benchmarks:

Number([num].toFixed(precision)) vs scaling + Math.round() (version: 0)

Number([num].toFixed(precision)) vs scaling + Math.round()

Comparing performance of: toFixed(4) vs Scale and round

Created: 2 years ago by: Guest

Jump to the latest result

toFixed(4)

Scale and round

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):