blacknode-cuda

Real GPU compute nodes for Blacknode.

This is a Blacknode extension package — it does not run on its own. It plugs CUDA compute blocks into the Blacknode visual workflow editor: kernels compile and execute on your local NVIDIA GPU and report measured timings, CPU baselines, speedups, and correctness checks.

Requirements

• The Blacknode main app
• An NVIDIA GPU with a CUDA 12.x driver (for actual compute)
• Python deps from requirements.txt (CuPy, NumPy, Pillow)

No GPU? The package still installs and loads fine — every node returns a structured "GPU not available" result instead of failing the graph, so workflows built with these nodes stay viewable and editable on any machine.

Install

From the Blacknode repo root, the one-liner:

blacknode packages install git@github.com:temiroff/blacknode-cuda.git

Or by hand:

git clone git@github.com:temiroff/blacknode-cuda.git packages/blacknode-cuda
pip install -r packages/blacknode-cuda/requirements.txt

Then restart Blacknode, or press Reload in the editor's Packages tab. Verify with:

blacknode packages list
# blacknode-cuda 0.1.0 [ok] 8 nodes  .../packages/blacknode-cuda

The nodes appear in the editor palette under the NVIDIA GPU category, and the example workflows show up in the Templates tab.

The nodes

Node	What it does
CUDAKernelLab	Curated GPU ops (vector add, saxpy, matmul, softmax, FFT, mandelbrot, monte-carlo π, ...) with measured GPU vs CPU timings and a NumPy correctness check
CUDACustomKernel	Write your own CUDA C kernel in the node, compiled at runtime with NVRTC (cupy.RawKernel) — includes starter templates
CUDAImageFilter	GPU image filters (grayscale, gaussian blur, sobel edges, invert, ...) wired to Blacknode's image ports
TensorCoreGEMM	WMMA Tensor Core half-precision matrix multiply via NVRTC
CUTLASS / CUTLASSGemm	CUTLASS GEMM running through Blacknode's sandboxed worker
GPUCapability	Detect the local GPU: name, compute capability, memory, driver
GPURequirement	Gate a workflow on a minimum GPU capability (preflight check)

Templates

Ready-made workflows in templates/, loadable from the editor's Templates tab:

• NVIDIA CUDA Lab — run and benchmark the curated op catalogue
• GPU Image Filter — load an image, filter it on the GPU, view the result
• CUTLASS GPU Burn — sustained CUTLASS GEMM benchmark
• CUTLASS Image Showcase — convolution path on real images

Updating / removing

cd packages/blacknode-cuda && git pull     # update
rm -rf packages/blacknode-cuda             # remove — base Blacknode keeps working

Development

After loading, the modules are importable through Blacknode's stable package alias:

from blacknode.pkg.blacknode_cuda import cuda

The suite in tests/ runs automatically when you run pytest from the Blacknode repo root (the core collects packages/*/tests/). GPU-dependent tests skip cleanly on machines without CuPy or an NVIDIA GPU.

This package is also the reference implementation for writing your own Blacknode extension package — see docs/packages.md for the manifest format and discovery rules.

License

Apache-2.0, same as Blacknode.