ringkernel-cuda

NVIDIA CUDA backend for RingKernel.

Overview

This crate provides GPU compute support for RingKernel using NVIDIA CUDA via the cudarc library. It implements the RingKernelRuntime trait for launching and managing persistent GPU kernels.

Requirements

• NVIDIA GPU with Compute Capability 7.0 or higher (Volta, Turing, Ampere, Ada, Hopper)
• CUDA Toolkit 11.0 or later
• Linux (native) or Windows (WSL2 with limitations)

Features

• Persistent kernel execution using cooperative groups
• Lock-free message queues in GPU global memory
• PTX compilation at runtime via NVRTC
• Multi-GPU device enumeration
• Stencil kernel loading and execution

Usage

use ringkernel_cuda::CudaRuntime;

#[tokio::main]
async fn main() -> Result<(), Box<dyn std::error::Error>> {
    // Check availability first
    if !ringkernel_cuda::is_cuda_available() {
        eprintln!("No CUDA device found");
        return Ok(());
    }

    let runtime = CudaRuntime::new().await?;
    let kernel = runtime.launch("processor", Default::default()).await?;

    // Process messages...

    kernel.terminate().await?;
    runtime.shutdown().await?;
    Ok(())
}

Stencil Kernel Loading

For pre-transpiled CUDA kernels:

use ringkernel_cuda::{StencilKernelLoader, LaunchConfig};

let loader = StencilKernelLoader::new(&cuda_device);
let kernel = loader.load_from_source(cuda_source)?;

let config = LaunchConfig {
    grid: (grid_x, grid_y, 1),
    block: (16, 16, 1),
    shared_mem: 0,
};

kernel.launch(&config, &[&input_buf, &output_buf])?;

Exports

Platform Notes

Native Linux: Full support for persistent kernels using CUDA cooperative groups.

WSL2: Persistent kernels may not work due to cooperative group limitations. Falls back to event-driven execution.

Windows Native: Not currently supported. Use WSL2.

Testing

# Requires NVIDIA GPU
cargo test -p ringkernel-cuda --features cuda

License

Apache-2.0