Benchmark: a * a vs. a ** 2 vs. Math.pow(a, 2)

Tests:

a * a
3.14 * 3.14
a ** 2
3.14 ** 2
Math.pow(a, 2)
Math.pow(3.14, 2)

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
a * a
a ** 2
Math.pow(a, 2)

Fastest: N/A

Slowest: N/A

Autogenerated LLM Summary (model llama3.2:3b, generated 26 days ago):

LLMs can make mistakes. Check important info.

Let's break down the provided benchmark and its components.

**Benchmark Definition**

The Benchmark Definition json represents three different approaches to calculate the square of a number:

1. `a * a` (multiplication)
2. `a ** 2` (exponentiation with the exponent being 2)
3. `Math.pow(a, 2)` (using the built-in `Math.pow` function)

These three approaches are compared to determine which one is the most efficient.

**Options Compared**

The options compared in this benchmark are:

* Multiplication (`a * a`)
* Exponentiation with a small exponent (`a ** 2`)
* Using the built-in `Math.pow` function (`Math.pow(a, 2)`)

Each of these approaches has its own pros and cons:

1. **Multiplication (`a * a`)**:
	* Pros: Simple and straightforward.
	* Cons: Requires two multiplications, which can be slower than other methods for large numbers.
2. **Exponentiation with a small exponent (`a ** 2`)**:
	* Pros: Faster than multiplication for most numbers, especially when the base is larger than 10.
	* Cons: May not be as fast as `Math.pow` for very small exponents or for decimal numbers.
3. **Using the built-in `Math.pow` function (`Math.pow(a, 2)`)**:
	* Pros: Fast and efficient, especially when dealing with large numbers or decimal numbers.
	* Cons: May have overhead due to the use of a dedicated function.

**Library Used**

The `Math.pow` function is part of the JavaScript Math library. Its purpose is to raise a number to a given power.

**Special JS Feature/Syntax**

There is no special JS feature or syntax used in this benchmark beyond what's already mentioned (`Math.pow`). However, it's worth noting that JavaScript engines can optimize certain expressions or functions under the hood, which might affect the performance results.

**Other Alternatives**

Some alternative approaches to calculate the square of a number could include:

* Using a dedicated library or function like `lodash.pow` (if using Lodash)
* Implementing a custom exponentiation algorithm
* Using bitwise operations (although this is generally not recommended due to its complexity and potential performance issues)

Keep in mind that these alternatives would likely change the benchmark's results, as they might be more efficient for certain use cases.

I hope this explanation helps you understand the Benchmark Definition and its components!

Latest run results:

Run details: (Test run date: one year ago)

User agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/118.0.0.0 Safari/537.36

Browser/OS: Chrome 118 on Windows

View result in a separate tab

Test name	Executions per second
a * a	1066949504.0 Ops/sec
a ** 2	1062404224.0 Ops/sec
Math.pow(a, 2)	14585515.0 Ops/sec

Related benchmarks:

a * a vs. a ** 2 vs. Math.pow(a, 2) (version: 0)

Comparing performace of: a * a vs. a ** 2 vs. Math.pow

Comparing performance of: a * a vs a ** 2 vs Math.pow(a, 2)

Created: one year ago by: Guest

Jump to the latest result

a * a

a ** 2

Math.pow(a, 2)

Suite status: <idle, ready to run>

Experimental features:

Fastest: N/A

Slowest: N/A

Autogenerated LLM Summary (model llama3.2:3b, generated 26 days ago):