tensorlogic-oxicuda-rng

GPU-accelerated random number generation for TensorLogic with pure-Rust CPU fallback.

Provides a unified RngEngine that seamlessly dispatches to either a pure-Rust CPU backend (PCG-XSH-RR + Box-Muller) or an OxiCUDA GPU backend — chosen at construction time with no API change.

Feature flags

Feature	Default	Effect
`cpu`	yes	Pure-Rust CPU RNG via `scirs2-core::random` (PCG-XSH-RR + Box-Muller).
`gpu`	no	Enables `oxicuda-rand`, `oxicuda-driver`, `oxicuda-memory`. Requires an NVIDIA driver at runtime — no CUDA SDK needed.

Quick start

use tensorlogic_oxicuda_rng::{RngEngine, RngEngineKind, RngError};

fn main() -> Result<(), RngError> {
    // CPU path (no NVIDIA driver required)
    let mut rng = RngEngine::new(RngEngineKind::Cpu, 42)?;

    let mut uniform = vec![0f32; 1024];
    rng.uniform_f32(&mut uniform)?;

    let mut normal = vec![0f32; 1024];
    rng.normal_f32(&mut normal, 0.0, 1.0)?;

    let mut mask = vec![0u8; 1024];
    rng.bernoulli(&mut mask, 0.5)?;

    println!("is_gpu: {}", rng.is_gpu()); // false
    Ok(())
}

API

`RngEngine`

Method	Description
`new(kind, seed)`	Create engine for `Cpu` or `Gpu` backend
`kind()`	Returns `RngEngineKind` of this engine
`is_gpu()`	Returns `true` if running on GPU
`uniform_f32(out)`	Fill slice with samples ∈ [0, 1)
`normal_f32(out, mean, std_dev)`	Fill slice with Gaussian samples
`bernoulli(out, p)`	Fill `u8` slice with 0/1 Bernoulli samples

`RngEngineKind`

Variant	Description
`Cpu`	Pure-Rust PCG-XSH-RR generator
`Gpu`	OxiCUDA GPU RNG (requires `gpu` feature and NVIDIA driver)

Requirements

CPU features work on all platforms (pure Rust, no native deps)
GPU features require --features gpu and an NVIDIA GPU with CUDA driver at runtime

License

Apache-2.0 — see LICENSE for details.

tensorlogic-oxicuda-rng 0.1.0