Universal RNG

A collection of efficient pseudo-random number generators (PRNGs) implemented in pure Rust. This crate provides a wide variety of algorithms, ranging from standard Mersenne Twister to modern high-performance generators like Xoshiro and Philox.

Installation

Add this to your Cargo.toml:

[dependencies]
urng = "0.4.1"

Supported Generators

Most generators implement either the Rng32 or Rng64 trait, providing a unified interface. SIMD generators have their own bulk-generation API and are listed separately.

32-bit Generators (urng::rng32)

Implement Rng32, output u32 natively.

64-bit Generators (urng::rng64)

Implement Rng64, output u64 natively.

¹ In the urng::rng64::xoroshiro submodule (not re-exported at urng::rng64).

Other (urng::rng128)

• Xorshift128: 128-bit state Xorshift, implements Rng32 (outputs u32).

SIMD Generators

These generators do not implement Rng32/Rng64 and instead expose a bulk-generation API.

AVX-512 (avx512f)

AVX2

Sampler

Requires the sampler feature.

Weighted random index selection. Two implementations are provided for each bit-width, both implementing the Sampler32 / Sampler64 trait (urng::sampler).

SeedGen

Requires the seedgen feature.

Hardware-noise-assisted seed generation. Wraps an existing Rng32/Rng64 and mixes in hardware noise (RDSEED/RDRAND on x86/x86_64, timestamp fallback elsewhere) via a Murmur3-style hash.

next_seed_pair() returns (raw, processed) — the raw hardware value and the mixed seed.

Usage Examples

Most generators expose the same basic workflow: create an instance with new, then use nextu, nextf, randi, randf, or choice depending on the output type you need. SIMD and counter-based generators return fixed-size arrays instead of single values.

Basic Usage

use urng::rng64::{Xoshiro256Pp, SplitMix64};
use urng::rng::Rng64; // Import trait for common methods

fn main() {
    // 1. Initialize with a seed
    let mut rng = Xoshiro256Pp::new(12345);

    // 2. Generate random numbers
    let val_u64 = rng.nextu();
    println!("u64: {}", val_u64);

    let val_f64 = rng.nextf(); // [0.0, 1.0)
    println!("f64: {}", val_f64);

    // 3. Generate within a range
    let val_range = rng.randi(1, 100);
    println!("Integer (1-100): {}", val_range);

    // 4. Seeding with SplitMix64 (common pattern)
    // If you need to seed a large state generator from a single u64
    let mut sm = SplitMix64::new(9999);
    let seed_val = sm.nextu();
    let mut rng2 = Xoshiro256Pp::new(seed_val);
}

Weighted Choice (BST)

The crate includes a binary-search-tree based weighted selector for efficient sampling.

use urng::bst::choice;
use urng::rng64::Xoshiro256Pp;

fn main() {
    let mut rng = Xoshiro256Pp::new(42);
    let items = vec!["Common", "Rare", "Epic"];
    let weights = vec![100.0, 10.0, 1.0];

    // Select an item based on weights
    if let Some(item) = choice(&mut rng, weights, &items) {
        println!("You got: {}", item);
    }
}

Using Macros

For convenience, you can use the provided macros (must import urng::*). These macros automatically initialize the generator with a system-seeded state.

use urng::{next, rand}; // Import macros

fn main() {
    // next! generates a single random number
    // Format: [algorithm][bits][u/f]
    let u = next!(mt64u); // Returns u64 using Mersenne Twister 64
    let f = next!(xor32f); // Returns f32 using Xorshift 32

    // rand! generates a number within a range
    // Format: [algorithm][bits][i/f]; [min], [max]
    let r = rand!(xor32i; 1, 10); // Returns i32 in range [1, 10]
    let f2 = rand!(mt64f; 0.0, 1.0); // Returns f64 in range [0.0, 1.0)
}

C ABI

This crate exports a C-compatible ABI generic interface. Each generator has corresponding:

• _new
• _free
• _next_uXXs (bulk generation)
• _next_fXXs (bulk generation)

Example for Mt19937:

void* mt19937_new(uint32_t seed, size_t warm);
void mt19937_next_u32s(void* ptr, uint32_t* out, size_t count);
void mt19937_free(void* ptr);