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# cuda

Typed bindings to the **CUDA Runtime** and **cuBLAS** for NVIDIA GPU compute.
This is plain C FFI over the vendor SDK: C+ stays a *consumer* of CUDA, with no
kernel language and nothing to reimplement. For the wider GPU/numerics picture
and the backend matrix, see [GPU & numerics](/docs/gpu-numerics).

Three sub-modules, plus a `cuda/cuda` facade that re-exports the types:

- `cuda/runtime` — device management (`device_count`, `set_device`,
  `synchronize`) and the `CudaError` type (`.code()`, `.message()`).
- `cuda/buffer` — a `DeviceBuffer` owns device memory and frees it on `Drop`
  (`cudaFree`), so device allocations follow the same ownership rules as host
  memory. Allocate with `alloc(bytes:)`; move bytes with `.write(from:, bytes:)`
  / `.read(to:, bytes:)`.
- `cuda/cublas` — a cuBLAS `Handle` (created via `handle()`, freed on `Drop`
  via `cublasDestroy_v2`) exposing the dense Level-3 / Level-2 routines `sgemm` and
  `sgemv`, **column-major**, matching the cuBLAS ABI exactly.

```cplus
import "cuda/runtime"  as rt;
import "cuda/buffer"   as buf;
import "cuda/cublas"   as blas;
import "stdlib/result" as result;

// hostA, hostB, hostC are host *u8 buffers laid out column-major.

// A cuBLAS context, created once; Drop = cublasDestroy_v2.
guard let result::Result[blas::Handle, rt::CudaError]::Ok(h) = blas::handle() else { return 1 as i32; };

// `alloc` returns a value-or-reason Result; each DeviceBuffer frees its
// device memory on scope exit (Drop = cudaFree).
guard let result::Result[buf::DeviceBuffer, rt::CudaError]::Ok(dA) = buf::alloc(bytes: 16 as usize) else { return 2 as i32; };
guard let result::Result[buf::DeviceBuffer, rt::CudaError]::Ok(dB) = buf::alloc(bytes: 16 as usize) else { return 3 as i32; };
guard let result::Result[buf::DeviceBuffer, rt::CudaError]::Ok(dC) = buf::alloc(bytes: 16 as usize) else { return 4 as i32; };

// Stage inputs on the device. write/read return Option[CudaError] (None = ok).
let _ = dA.write(from: hostA, bytes: 16 as usize);
let _ = dB.write(from: hostB, bytes: 16 as usize);

// Column-major C = alpha*op(A)*op(B) + beta*C. Operands are device pointers;
// the call returns Option[CudaError] instead of trapping.
let _ = h.sgemm(
    blas::Op::N, blas::Op::N,
    2 as i32, 2 as i32, 2 as i32,
    1.0f32, dA.device_ptr(), 2 as i32, dB.device_ptr(), 2 as i32,
    0.0f32, dC.device_ptr(), 2 as i32);
let _ = rt::synchronize();

let _ = dC.read(to: hostC, bytes: 16 as usize);
```

Because cuBLAS is column-major, lay out matrices column-major (or pass the
transpose flags) exactly as you would from C. The `Handle` and every
`DeviceBuffer` release their resources deterministically at scope exit; there is
no explicit teardown to forget.

## Linking

The CUDA libraries usually live in `lib64`, outside the linker's default search
path. List the directory in your manifest so it resolves at both link and run
time without `LD_LIBRARY_PATH`:

```toml
[link]
search-paths = ["/usr/local/cuda/lib64"]
```

For the CPU fallback and a result-checking reference, see
[cblas](/docs/packages/cblas) and [accelerate](/docs/packages/accelerate).