Frequently Asked Questions

General Questions

Do I need an NVIDIA GPU to use Sarek?

No. While Sarek supports CUDA for NVIDIA hardware, it also supports OpenCL and Vulkan, which work on AMD GPUs, Intel Integrated Graphics, and even some FPGAs. If no GPU is available, Sarek can fall back to the Native CPU backend.

Can I run Sarek on macOS?

Yes. Sarek has a dedicated Metal backend for Apple Silicon (M1/M2/M3) and Intel-based Macs. It also supports OpenCL on macOS.

Does Sarek work on Windows?

Sarek has limited testing on Windows, but it should work via the OpenCL and Vulkan backends. Using WSL2 is recommended for the best experience.

Technical Questions

What is the overhead of using OCaml for GPU programming?

Sarek uses a JIT compilation approach for GPU backends. The logic you write in OCaml is compiled into native GPU code (CUDA C, GLSL, etc.). Once the kernel is compiled and cached, the execution performance is comparable to hand-written C/CUDA code.

Does the OCaml Garbage Collector interfere with the GPU?

No. The GPU has its own memory. Data is explicitly transferred between the OCaml heap and the GPU memory via Vector objects. The GC manages the OCaml “handle” to the GPU memory, but does not touch the data residing on the device.

Can I use custom OCaml types in my kernels?

Yes. By using the [@@sarek.type] attribute on record definitions, Sarek automatically generates the corresponding C structures for the GPU and handles the memory layout.

Is OCaml 5 required?

Yes. The latest version of Sarek leverages OCaml 5 Effects and Domains for the Native CPU backend to provide high-performance parallel execution on multi-core processors.

CUDA Backend Questions

What CUDA version do I need for newer GPUs?

For Blackwell architecture GPUs (RTX 5000 series, compute capability 12.0):

• Minimum: CUDA 12.9 with driver 575+
• Recommended: CUDA 13.1 with driver 580+

Sarek automatically handles forward compatibility by compiling PTX for compute_90 and letting the driver JIT-compile for newer architectures.

I get CUDA_ERROR_UNKNOWN(222) when loading kernels. What’s wrong?

This error typically occurs with newer GPU architectures when there’s a version mismatch:

1. Check your versions:

   nvidia-smi        # Shows driver version and API level
   nvcc --version    # Shows CUDA toolkit version
   

2. Common causes:
   • CUDA 13.1 with driver < 580 (downgrade to CUDA 12.9)
   • CUDA toolkit older than 12.9 on Blackwell GPUs (upgrade toolkit)
3. Verify your setup:

   dune exec -- sarek-device-info
   

What does “CUDA Version” in nvidia-smi mean?

The “CUDA Version” shown by nvidia-smi (e.g., “12.9”) is the maximum CUDA runtime API version your driver supports, not your installed toolkit version. It’s normal and expected for these to differ. For example, driver 575 supports CUDA API 12.9, even if you have CUDA toolkit 13.1 installed (though it won’t work fully without driver 580+).

How do I list available CUDA devices?

Use the device info utility:

dune exec -- sarek-device-info

This shows all detected devices across all backends (CUDA, OpenCL, Vulkan, Metal, Native, Interpreter) with their capabilities.