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

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 - Math.sqrt(1 - Math.random()))) + min;
}

Tests:

function A (regular)
randIntA1(10)
function A (with Bitwise Double Not)
randIntA2(10)
function B (regular)
randIntB1(10)
function B (with Bitwise Double Not)
randIntB2(10)

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
function A (regular)
function A (with Bitwise Double Not)
function B (regular)
function B (with Bitwise Double Not)

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 dive into the benchmark!

**Benchmark Definition:**

The provided JSON defines two main functions for generating random integers:

1. `randIntA1(max, min = 0)`: This function uses `Math.random()` to generate a random number between `min` and `max`. The result is then scaled using `Math.abs`, multiplied by the range `(1 + max - min)` and finally added to `min`.
2. `randIntB1(max, min = 0)`: This function generates a random number between `min` and `max` using the arcsine method, which involves taking the inverse sine of a uniform random variable.

There are two variants for each function:

* `with Bitwise Double Not` (functions A2 and B2): These functions use bitwise operations to improve performance. Specifically:
	+ For `randIntA1`, the expression `(Math.random() - Math.random())` is used instead of just `Math.random()`. This takes advantage of CPU architectures where subtracting two identical values produces a non-zero result, which can be optimized by the processor.
	+ For `randIntB1`, the arcsine method is applied to `(1 - Math.random())`, which is equivalent to taking the inverse sine of a uniform random variable.

**Options Compared:**

The benchmark compares the performance of these four functions:

* `function A (regular)`: `randIntA1(max, min = 0)` without bitwise optimization
* `function A (with Bitwise Double Not)`: `randIntA1(max, min = 0)` with bitwise optimization
* `function B (regular)`: `randIntB1(max, min = 0)`
* `function B (with Bitwise Double Not)`: `randIntB1(max, min = 0)` with bitwise optimization

**Pros and Cons:**

* **`randIntA1`**: Simple and easy to implement, but may not be as efficient as the optimized version.
	+ Pros: Easy to understand, widely supported
	+ Cons: May be slower than `randIntB1`
* **`randIntB1`**: More complex and less intuitive, but can be faster for large ranges.
	+ Pros: Can be faster for large ranges, more efficient
	+ Cons: More complex, may require more CPU cycles
* **Bitwise optimization**:
	+ Pros: Can improve performance, especially on certain CPU architectures
	+ Cons: May add complexity, can make code harder to understand

**Library and Purpose:**

There is no specific library mentioned in the provided JSON. However, `Math.random()` is a built-in JavaScript function that generates random numbers.

**Special JS Feature or Syntax:**

The use of bitwise double NOT (`~`) is a special operation in some CPU architectures, which can be exploited to improve performance. This syntax is not directly related to any specific JavaScript feature, but rather an optimization technique used by some compilers and processors.

**Other Considerations:**

* The benchmark uses the `executionsPerSecond` metric to measure performance, which may not accurately represent the actual usage scenarios.
* The results should be taken as a snapshot of performance on this specific machine and browser, and may not generalize to other platforms or environments.
* The use of different JavaScript versions (Firefox 84) can introduce variations in behavior due to engine-specific optimizations.

**Alternatives:**

Other alternatives for generating random integers include:

* `Math.random()` with a seed value for reproducibility
* Using a dedicated library like `Crypto.getRandomValues()`
* Implementing a custom random number generator using a different algorithm (e.g., Linear Congruential Generator)

Keep in mind that the choice of method depends on the specific requirements and constraints of your application.

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)	579096384.0 Ops/sec
function A (with Bitwise Double Not)	619639232.0 Ops/sec
function B (regular)	597727424.0 Ops/sec
function B (with Bitwise Double Not)	632134400.0 Ops/sec

Related benchmarks:

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

Comparing performance of: function A (regular) vs function A (with Bitwise Double Not) vs function B (regular) vs function B (with Bitwise Double Not)

Created: 3 years ago by: Guest

Jump to the latest result

function A (regular)

function A (with Bitwise Double Not)

function B (regular)

function B (with Bitwise Double Not)

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):