Benchmark: getRandomIntInRange

Tests:

Math.random
return Math.floor(Math.random() * 50000)
crypto.getRandomValues
const range = 5000 const randomBuffer = new Uint32Array(1); window.crypto.getRandomValues(randomBuffer); const randomNumber = randomBuffer[0]; const scaledRandomNumber = Math.floor(randomNumber / (Math.pow(2, 32) / range)); return scaledRandomNumber;

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
Math.random
crypto.getRandomValues

Fastest: N/A

Slowest: N/A

Latest run results:

No previous run results

This benchmark does not have any results yet. Be the first one to run it!

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

LLMs can make mistakes. Check important info.

**What is tested in the provided JSON?**

The provided JSON represents two individual test cases for measuring JavaScript microbenchmarks on MeasureThat.net. Each test case is designed to measure the performance of a specific JavaScript function or operation.

**Test Case 1: "Math.random"**

* The benchmark definition is simply `return Math.floor(Math.random() * 50000)`. This test case measures the execution time of generating a random integer between 0 and 50,000 using the built-in `Math.random()` function.
* The pros of this approach are:
	+ Simple and easy to understand.
	+ No additional setup or dependencies required.
* However, some cons include:
	+ The random number generation may not be truly random, as `Math.random()` uses a pseudorandom number generator algorithm that can be predictable in certain scenarios.
	+ This test case does not account for potential performance differences between different browsers or environments.

**Test Case 2: "crypto.getRandomValues"**

* The benchmark definition is more complex and involves generating a random integer using the `crypto` API:
```javascript
const range = 5000;
const randomBuffer = new Uint32Array(1);
window.crypto.getRandomValues(randomBuffer);
const randomNumber = randomBuffer[0];
const scaledRandomNumber = Math.floor(randomNumber / (Math.pow(2, 32) / range));
return scaledRandomNumber;
```
* The pros of this approach are:
	+ It generates truly random numbers using the `crypto` API.
	+ This test case accounts for potential performance differences between different browsers or environments that may affect the quality of randomness.

**Library and Purpose**

In Test Case 2, the library being used is the `crypto` API. The purpose of this library is to provide a way to generate cryptographically secure random numbers. In modern browsers, `crypto.getRandomValues()` is designed to be more secure than other methods, such as `Math.random()`, by using the operating system's entropy pool.

**Other Considerations**

When writing benchmarks like these, it's essential to consider several factors:

* **Warm-up and idle time**: Ensure that the test case is properly warmed up before running. Idle time can lead to inaccurate results.
* **Number of executions**: The number of executions should be sufficient to demonstrate performance differences between different approaches or implementations.
* **Browser and environment variations**: Consider using multiple browsers, environments, or device types to ensure that the results are representative.

**Alternatives**

If you're looking for alternatives to MeasureThat.net, consider:

* **jsperf**: A JavaScript benchmarking platform that allows you to create custom benchmarks and compare different code snippets.
* **BenchmarkJS**: A library for creating browser-based benchmarks. It provides a simple way to define and run benchmarks, including support for various test cases and execution modes.

These alternatives offer similar functionality to MeasureThat.net but may have additional features or options depending on your specific needs.

Related benchmarks:

getRandomIntInRange (version: 0)

Comparing performance of: Math.random vs crypto.getRandomValues

Created: 2 years ago by: Guest

Jump to the latest result

Math.random

crypto.getRandomValues

Suite status: <idle, ready to run>

Fastest: N/A

Slowest: N/A

No previous run results

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