Benchmark: roundDecimal() with Math.pow vs. Unary and toPrecision()

Script Preparation code:

function roundDecimalPow(value, decimals) {
  const factor = Math.pow(10, decimals);
  return Math.round(value * factor) / factor;
}

function roundDecimalUnaryToPrecision(value, decimals) {
  return +value.toPrecision(decimals);
}

function roundDecimalParseFloatToPrecision(value, decimals) {
  return parseFloat(value.toPrecision(decimals));
}

Tests:

roundDecimalPow()
roundDecimalPow(Math.random(), 2)
roundDecimalUnaryToPrecision()
roundDecimalUnaryToPrecision(Math.random(), 2)
roundDecimalParseFloatToPrecision()
roundDecimalParseFloatToPrecision(Math.random(), 2)

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
roundDecimalPow()
roundDecimalUnaryToPrecision()
roundDecimalParseFloatToPrecision()

Fastest: N/A

Slowest: N/A

Autogenerated LLM Summary (model llama3.2:3b, generated 27 days ago):

LLMs can make mistakes. Check important info.

Let's dive into the world of JavaScript microbenchmarks!

**What is being tested?**

MeasureThat.net is testing three different approaches to rounding decimal numbers:

1. `Math.pow` with unary operator (`roundDecimalPow`)
2. Unary operator (`roundDecimalUnaryToPrecision`)
3. Using `parseFloat` and `toPrecision` methods together (`roundDecimalParseFloatToPrecision`)

**Options compared:**

These three options are being compared in terms of their performance, speed, and possibly other factors like memory usage or accuracy.

**Pros and Cons of each approach:**

1. **Math.pow with unary operator (roundDecimalPow)**:
	* Pros:
		+ Simple and straightforward implementation.
		+ Might be faster due to the use of `Math.pow`'s built-in caching.
	* Cons:
		+ May not be as efficient for large decimal numbers due to the unnecessary multiplication.
2. **Unary operator (roundDecimalUnaryToPrecision)**:
	* Pros:
		+ More concise and expressive implementation, using a common JavaScript idiom.
	* Cons:
		+ Might be slower due to the overhead of creating a string with `toPrecision`.
3. **Using parseFloat and toPrecision methods together (roundDecimalParseFloatToPrecision)**:
	* Pros:
		+ More explicit and readable implementation, separating concerns between parsing and formatting.
	* Cons:
		+ May introduce additional overhead due to the creation of temporary strings.

**Library usage:**

None of the test cases use any external libraries. However, it's worth noting that `toPrecision` is a built-in JavaScript method.

**Special JS feature/syntax:**

There are no special features or syntax used in this benchmark, as far as can be determined from the provided code.

**Other alternatives:**

If you were to implement these rounding decimal numbers using alternative approaches, some possible options could include:

* Using `Number.EPSILON` for relative tolerance-based rounding
* Implementing a custom rounding algorithm, such as Banker's Rounding or Round Half to Even
* Utilizing SIMD instructions (if available) for parallelization

Keep in mind that these alternatives might not be directly comparable to the original implementation in this benchmark.

Latest run results:

Run details: (Test run date: 3 years ago)

User agent: Mozilla/5.0 (Macintosh; Intel Mac OS X 11_2_3) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/89.0.4389.128 Safari/537.36

Browser/OS: Chrome 89 on Mac OS X 11.2.3

View result in a separate tab

Test name	Executions per second
roundDecimalPow()	2222264.2 Ops/sec
roundDecimalUnaryToPrecision()	2032386.2 Ops/sec
roundDecimalParseFloatToPrecision()	1725403.9 Ops/sec

Related benchmarks:

roundDecimal() with Math.pow vs. Unary and toPrecision() (version: 0)

Comparing performance of: roundDecimalPow() vs roundDecimalUnaryToPrecision() vs roundDecimalParseFloatToPrecision()

Created: 3 years ago by: Guest

Jump to the latest result

roundDecimalPow()

roundDecimalUnaryToPrecision()

roundDecimalParseFloatToPrecision()

Suite status: <idle, ready to run>

Experimental features:

Fastest: N/A

Slowest: N/A

Autogenerated LLM Summary (model llama3.2:3b, generated 27 days ago):