Benchmark: Sqare Root - MeasureThat.net

Tests:

Math.sqrt
for (let i = 100; i--;) Math.sqrt(i)
Math.pow
for (let i = 100; i--;) Math.pow(i, .5)
Power Op
for (let i = 100; i--;) i ** .5

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
Math.sqrt
Math.pow
Power Op

Fastest: N/A

Slowest: N/A

Latest run results:

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

User agent: Mozilla/5.0 (Macintosh; Intel Mac OS X 10.16; rv:84.0) Gecko/20100101 Firefox/84.0

Browser/OS: Firefox 84 on Mac OS X 10.16

View result in a separate tab

Test name	Executions per second
Math.sqrt	6303657.0 Ops/sec
Math.pow	6320410.0 Ops/sec
Power Op	6801122.0 Ops/sec

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

LLMs can make mistakes. Check important info.

Measuring the performance of different mathematical operations in JavaScript can be fascinating.

**Benchmark Overview**

The provided JSON represents a simple benchmarking script that compares the performance of three different methods to calculate the square root of a number: `Math.sqrt`, `Math.pow` with an exponent of 0.5, and a custom "Power Op" using the exponentiation operator (`**`). The benchmark is designed to test which method provides the best performance.

**Options Compared**

There are three options compared in this benchmark:

1. **`Math.sqrt`**: This method uses the square root function provided by the JavaScript Math object.
2. **`Math.pow(i, 0.5)`**: This method uses the `pow()` function from the JavaScript Math object to raise a number to a fractional exponent (in this case, 0.5).
3. **`i ** .5`**: This method uses the exponentiation operator (`**`) to raise a number to a fractional exponent.

**Pros and Cons of Each Approach**

1. **`Math.sqrt`**:
	* Pros: Simple, efficient, and widely supported.
	* Cons: May not be as accurate as other methods for very large or small numbers due to the limitations of floating-point arithmetic.
2. **`Math.pow(i, 0.5)`**:
	* Pros: More accurate than `Math.sqrt` for some edge cases, but may still suffer from floating-point precision issues.
	* Cons: Uses more operations and may be slightly slower than `Math.sqrt`.
3. **`i ** .5`**:
	* Pros: Simple, efficient, and widely supported.
	* Cons: May not be as accurate as other methods for very large or small numbers due to the limitations of exponentiation operator behavior.

**Library Usage**

There are no external libraries used in this benchmark.

**Special JavaScript Features or Syntax**

None of the test cases use special JavaScript features or syntax that require specific knowledge. The operations are simple and well-supported by modern JavaScript engines.

**Other Considerations**

When writing benchmarks like this, consider the following factors:

* **Test size**: Make sure the test size is representative of real-world usage to ensure accurate results.
* **Garbage collection**: Test your benchmark in a context that minimizes garbage collection overhead to get an accurate picture of performance.
* **Browser and hardware variations**: Run your benchmark on multiple browsers and devices to account for potential hardware and software differences.

**Alternative Approaches**

If you want to explore alternative approaches, consider the following:

* Use a Just-In-Time (JIT) compiler like SpiderMonkey (in Firefox) or V8 (in Chrome) to optimize your code for performance.
* Write a native extension using languages like C++ or Rust to bypass JavaScript limitations and directly interact with hardware resources.
* Utilize specialized libraries or frameworks that provide optimized mathematical functions, such as BLAS (Basic Linear Algebra Subprograms) or FFTW (Fast Fourier Transform in the West).

Keep in mind that each approach has its pros and cons, and the choice ultimately depends on your specific use case and requirements.

Related benchmarks:

Sqare Root (version: 0)

Comparing performance of: Math.sqrt vs Math.pow vs Power Op

Created: 3 years ago by: Guest

Jump to the latest result

Math.sqrt

Math.pow

Power Op

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):