Benchmark: math.pow vs multiply vs exponentiation

Tests:

Multiply
x = 49.745232942 * 49.745232942
Exponentiation
x = 49.745232942 ** 2
Math.pow
x = Math.pow(49.745232942, 2)

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
Multiply
Exponentiation
Math.pow

Fastest: N/A

Slowest: N/A

Latest run results:

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

User agent: Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/605.1.15 (KHTML, like Gecko) Version/26.3.1 Safari/605.1.15

Browser/OS: Safari 26 on Mac OS X 10.15.7

View result in a separate tab

Test name	Executions per second
Multiply	588284736.0 Ops/sec
Exponentiation	611145792.0 Ops/sec
Math.pow	550824576.0 Ops/sec

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

LLMs can make mistakes. Check important info.

I'll explain the benchmark in detail.

The provided JSON represents a JavaScript microbenchmarking test case, where users can compare the performance of different approaches to calculate the square of a number.

**What is tested?**

Three test cases are defined:

1. **Multiply**: This test case uses simple multiplication (`x = 49.745232942 * 49.745232942`) to calculate the square of a number.
2. **Exponentiation**: This test case uses the exponentiation operator (`**`) to calculate the square of a number (`x = 49.745232942 ** 2`).
3. **Math.pow**: This test case uses the `Math.pow()` function from JavaScript's built-in Math library to calculate the square of a number (`x = Math.pow(49.745232942, 2)`).

**Options compared**

These three approaches are compared in terms of their performance, measured by the number of executions per second.

**Pros and Cons of each approach:**

1. **Multiply**: This approach is simple and straightforward, but it may suffer from precision issues due to floating-point arithmetic.
	* Pros: Easy to implement, no additional libraries required.
	* Cons: May have precision errors, can be slower than other approaches.
2. **Exponentiation**: Using the exponentiation operator (`**`) is a concise way to calculate squares, but it may not work correctly for negative numbers or complex numbers.
	* Pros: Concise and expressive, can be faster than multiplication due to optimized engine support.
	* Cons: May not work correctly for certain input types, requires understanding of operator precedence.
3. **Math.pow**: Using the `Math.pow()` function is a robust way to calculate squares, as it handles various input types correctly, including negative numbers and complex numbers.
	* Pros: Robust and accurate, uses optimized engine support.
	* Cons: May be slower than exponentiation due to additional overhead.

**Library usage**

None of these test cases explicitly use any external libraries. The `Math.pow()` function is part of the JavaScript standard library.

**Special JS feature/syntax**

No special JavaScript features or syntax are used in this benchmark. It only uses standard JavaScript operators and functions.

**Other alternatives**

If you wanted to compare other approaches, you could consider:

1. **Using a dedicated mathematical library**: Libraries like NumJS or MathJS provide optimized implementations of various mathematical operations, including exponentiation.
2. **Implementing custom algorithms**: You could write your own implementation of square calculation using loops, bitwise operators, or other techniques.
3. **Using parallel processing**: If you have multiple CPU cores available, you could use parallel processing techniques to calculate squares in parallel, potentially leading to faster execution times.

These alternatives would require additional code changes and may not be as straightforward to implement as the existing benchmark.

Related benchmarks:

math.pow vs multiply vs exponentiation (version: 0)

Comparing performance of: Multiply vs Exponentiation vs Math.pow

Created: 2 years ago by: Guest

Jump to the latest result

Multiply

Exponentiation

Math.pow

Suite status: <idle, ready to run>

Fastest: N/A

Slowest: N/A

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