Benchmark: Random Integer Generator (favors numbers closer to 0) 2

Script Preparation code:

// https://gamedev.stackexchange.com/a/116837
function randIntA1(max, min = 0) {
  return Math.floor(Math.abs(Math.random() - Math.random()) * (1 + max - min) + min);
}
function randIntA2(max, min = 0) {
  return ~~(Math.abs(Math.random() - Math.random()) * (1 + max - min) + min);
}
function randIntB1(max, min = 0) {
  return Math.floor((1 + max - min) * (1 - Math.sqrt(1 - Math.random()))) + min;
}
function randIntB2(max, min = 0) {
  return ~~((1 + max - min) * (1 - (1 - Math.random()) ** .5)) + min;
}

Tests:

function A (regular)
randIntA1(10)
function A (simplified)
randIntA2(10)
function B (regular)
randIntB1(10)
function B (simplified)
randIntB2(10)

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
function A (regular)
function A (simplified)
function B (regular)
function B (simplified)

Fastest: N/A

Slowest: N/A

Latest run results:

Run details: (Test run date: 3 years ago)

User agent: Mozilla/5.0 (Macintosh; Intel Mac OS X 10.16; rv:84.0) Gecko/20100101 Firefox/84.0

Browser/OS: Firefox 84 on Mac OS X 10.16

View result in a separate tab

Test name	Executions per second
function A (regular)	626748736.0 Ops/sec
function A (simplified)	623473728.0 Ops/sec
function B (regular)	595515456.0 Ops/sec
function B (simplified)	564578176.0 Ops/sec

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

LLMs can make mistakes. Check important info.

**Overview of the Benchmark**

The provided benchmark measures the performance of four different algorithms for generating random integers. The benchmarks are:

1. `randIntA1` and its simplified version `randIntA2`: These two functions use the formula `Math.floor(Math.abs(Math.random() - Math.random()) * (1 + max - min) + min)` to generate a random integer between `min` and `max`.
2. `randIntB1` and its simplified version `randIntB2`: These two functions use the formula `Math.floor((1 + max - min) * (1 - Math.sqrt(1 - Math.random()))) + min` to generate a random integer between `min` and `max`.

**Comparison of Algorithms**

The main difference between these algorithms is the use of mathematical operations to generate the random number. Here's a brief overview of each:

* `randIntA1` and `randIntA2`: These functions use the absolute value function (`Math.abs`) to scale down the range of random numbers generated by `Math.random()`. This reduces the likelihood of generating very small or large values, which can improve performance.
* `randIntB1` and `randIntB2`: These functions use the square root function (`Math.sqrt`) in combination with the absolute value function. The square root function is used to scale down the range of random numbers generated by `Math.random()`, while the absolute value function ensures that the result is always non-negative.

**Pros and Cons of Each Approach**

* `randIntA1` and `randIntA2`:
	+ Pros: Simple, easy to understand, and widely supported.
	+ Cons: May not be as efficient as other methods, especially for large ranges.
* `randIntB1` and `randIntB2`:
	+ Pros: Can be more efficient than the absolute value method, especially for large ranges.
	+ Cons: Use of square root function can introduce additional overhead.

**Library Used**

The benchmark uses no external libraries beyond JavaScript's built-in functions (`Math`). However, it does use a trick with `Math.random()` to generate two independent random numbers in a single call, which is an optimization technique often used in benchmarking tests.

**Special JS Features or Syntax**

There are no special JavaScript features or syntax used in this benchmark beyond the standard functions and data types. The focus is on comparing the performance of different algorithms for generating random integers.

**Other Alternatives**

For generating random numbers, other algorithms that could be considered include:

* Linear congruential generators (LCGs): These algorithms use a simple formula to generate a sequence of pseudorandom numbers.
* Mersenne twisters: These are high-quality, uniformly distributed random number generators that can be used for a wide range of applications.
* Xoshiro series: These are fast and efficient random number generators that can be used in various applications.

However, the specific algorithms used in this benchmark (`randIntA1`, `randIntA2`, `randIntB1`, and `randIntB2`) are designed to compare the performance of different methods for generating random integers within a small range.

Related benchmarks:

Random Integer Generator (favors numbers closer to 0) 2 (version: 0)

Comparing performance of: function A (regular) vs function A (simplified) vs function B (regular) vs function B (simplified)

Created: 3 years ago by: Guest

Jump to the latest result

function A (regular)

function A (simplified)

function B (regular)

function B (simplified)

Suite status: <idle, ready to run>

Experimental features:

Fastest: N/A

Slowest: N/A

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