Benchmark: a*a vs a**2 - MeasureThat.net

Script Preparation code:

var int = 100;
var float = 123.456;

Tests:

int a*a
int*int;
int a**2
int**2;
float a*a
float*float;
float a**2
float**2;

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
int a*a
int a**2
float a*a
float a**2

Fastest: N/A

Slowest: N/A

Autogenerated LLM Summary (model llama3.2:3b, generated 28 days ago):

LLMs can make mistakes. Check important info.

Let's break down the test cases and compare the different approaches.

**Benchmark Definition**

The benchmark is testing three types of operations:

1. `int*int` (integer multiplication)
2. `float*float` (floating-point number multiplication)
3. `int**2` (integer exponentiation) and `float**2` (floating-point number exponentiation)

These tests aim to compare the performance of different approaches for these mathematical operations.

**Options Compared**

There are two options being compared in each test case:

1. **Simple vs Optimized Code**: The code is either written as it would be written by a typical developer or optimized for better performance.
2. **No Optimization vs Inlining/Loop Unrolling**: The code is either left unoptimized or optimized using inlining and loop unrolling techniques.

**Pros and Cons of Different Approaches**

1. **Simple Code**:
	* Pros: Easy to understand, maintain, and debug.
	* Cons: May be slower due to the lack of optimization.
2. **Optimized Code**:
	* Pros: Can be faster than simple code, especially for large datasets or computationally intensive operations.
	* Cons: May require more expertise to write and maintain.
3. **Inlined/Loop Unrolled Code**:
	* Pros: Can significantly improve performance by reducing function calls and loop iterations.
	* Cons: Requires careful consideration of the algorithm's complexity and may lead to increased code size.

**Library Usage**

None of the provided test cases use any external libraries.

**Special JS Features/Syntax**

The tests don't explicitly mention using special JavaScript features or syntax. However, it's worth noting that some modern JavaScript engines, like V8 (used by Chrome), have optimized implementations for certain mathematical operations like exponentiation.

**Other Alternatives**

If you were to create your own benchmarking tool, consider the following alternatives:

1. **Google Benchmark**: A popular open-source library for benchmarking C++ code.
2. **Benchmark.js**: A lightweight JavaScript library for benchmarking web applications.
3. **Jest**: A popular testing framework that can also be used for benchmarking.

When creating a new benchmarking tool, consider the following:

1. **Use a standardized format** for defining benchmarks to ensure consistency across different tools and implementations.
2. **Provide options** for customizing the benchmarking process, such as the number of iterations, execution time limits, and reporting settings.
3. **Support multiple test cases** with varying complexity and optimization strategies to provide a comprehensive understanding of performance characteristics.

By following these guidelines, you can create a robust and user-friendly benchmarking tool that helps developers optimize their code for better performance.

Latest run results:

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

User agent: Mozilla/5.0 (Linux; Android 10; K) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/115.0.0.0 Mobile Safari/537.36

Browser/OS: Chrome Mobile 115 on Android

View result in a separate tab

Test name	Executions per second
int a*a	732123.7 Ops/sec
int a**2	1397115.4 Ops/sec
float a*a	720541.6 Ops/sec
float a**2	1396592.8 Ops/sec

Related benchmarks:

a*a vs a**2 (version: 0)

Comparing performance of: int a*a vs int a**2 vs float a*a vs float a**2

Created: 6 years ago by: Registered User

Jump to the latest result

int a*a

int a**2

float a*a

float a**2

Suite status: <idle, ready to run>

Experimental features:

Fastest: N/A

Slowest: N/A

Autogenerated LLM Summary (model llama3.2:3b, generated 28 days ago):