Benchmark: randoms - MeasureThat.net

HTML Preparation code:

<script>
  'use strict';
const m = 4294967296;
const a = 1664525;
const c = 1013904223;
var z = 6700417;

const lcg = function() {
  'use strict';
	z = (a * z + c) % m;
	return z / m;
};

var rv = 0;
  
</script>

Tests:

lcg
rv = lcg();
math.random
rv = Math.random();
crypto
{ let arr = new Uint32Array(1); crypto.getRandomValues(arr); rv = arr[0]; };

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
lcg
math.random
crypto

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 benchmark test cases and explain what is being tested.

**Benchmark Definition JSON**

The provided `benchmark definition` JSON represents a JavaScript microbenchmark. It defines two main parts:

1. **Script Preparation Code**: This section contains the initial setup code for the benchmark, including variable declarations and function definitions.
2. **Html Preparation Code**: This section contains the HTML code that will be used to execute the benchmark.

**Script Preparation Code Explanation**

The script preparation code is:
```javascript
'use strict';

const m = 4294967296;
const a = 1664525;
const c = 1013904223;
var z = 6700417;

const lcg = function() {
  'use strict';
  z = (a * z + c) % m;
  return z / m;
};

var rv = 0;
```
This code defines a simple Linear Congruential Generator (LCG) algorithm, which is a widely used pseudorandom number generator. The LCG algorithm takes three parameters: `m`, `a`, and `c`. In this case, the values are hardcoded.

The script also declares a variable `rv` to store the generated random value.

**Html Preparation Code Explanation**

The HTML preparation code is:
```html
<script>
  'use strict';
</script>
```
This code includes the `'use strict'` directive, which enables strict mode in JavaScript. This helps catch common errors and improves code security.

**Individual Test Cases**

There are three individual test cases:

1. **LCG (Linear Congruential Generator)**
```javascript
rv = lcg();
```
This test case measures the performance of the LCG algorithm.
2. **Math.random()**
```javascript
rv = Math.random();
```
This test case measures the performance of the `Math.random()` function, which is a built-in JavaScript function for generating random numbers.
3. **Crypto GetRandomValues()**
```javascript
{
  let arr = new Uint32Array(1);
  crypto.getRandomValues(arr);
  rv = arr[0];
}
```
This test case measures the performance of the `GetRandomValues()` function from the Web Cryptography API, which is used to generate cryptographically secure random numbers.

**Pros and Cons of Different Approaches**

* **LCG (Linear Congruential Generator)**: Pros:
	+ Simple and efficient algorithm.
	+ Fast execution time.
Cons:
	+ Not suitable for generating high-quality random numbers.
	+ Not designed for security use cases.
* **Math.random()**: Pros:
	+ Built-in JavaScript function with good performance.
	+ Easy to use.
Cons:
	+ May not be suitable for generating cryptographically secure random numbers.
	+ Can suffer from bias and non-uniformity in the generated values.
* **Crypto GetRandomValues()**: Pros:
	+ Generates high-quality, cryptographically secure random numbers.
	+ Suitable for security use cases.
Cons:
	+ Slower execution time compared to LCG or Math.random().
	+ May require additional setup and configuration.

**Other Considerations**

When choosing a method for generating random numbers, consider the specific requirements of your application. If you need high-quality, cryptographically secure random numbers, `Crypto GetRandomValues()` is likely the best choice. However, if you only need simple, non-secure random numbers, LCG or Math.random() may be sufficient.

**Alternative Methods**

Other methods for generating random numbers include:

* **Xorshift**: A fast and efficient algorithm for generating pseudorandom numbers.
* **Fortuna PRNG**: A high-quality, cryptographically secure pseudo-random number generator.
* **HMAC-based PRNG**: A method that uses a cryptographic hash function to generate pseudorandom numbers.

Keep in mind that each of these methods has its own pros and cons, and the choice ultimately depends on your specific requirements and constraints.

Latest run results:

Run details: (Test run date: 2 years ago)

User agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/100.0.4896.75 Safari/537.36

Browser/OS: Chrome 100 on Windows

View result in a separate tab

Test name	Executions per second
lcg	2589035.0 Ops/sec
math.random	2440215.5 Ops/sec
crypto	420616.4 Ops/sec

Related benchmarks:

randoms (version: 2)

Comparing performance of: lcg vs math.random vs crypto

Created: 6 years ago by: Registered User

Jump to the latest result

lcg

math.random

crypto

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):