CPU and GPU

CPU paths in nornir_imageregistration do not need CuPy. GPU support is optional.

• Developer machines: pip install -e "nornir-imageregistration[gpu]" --no-deps for CuPy. The Windows Pyre installer does not include it.

• Pairwise distances on the GPU use the gpu_cuvs extra. Nearest-neighbor search stays on SciPy below 4096 points, because a brute-force GPU distance is slower than a 2D tree at typical mesh sizes. At 4096 points or more the index uses cuVS. NORNIR_CUVS_NN_MIN_POINTS changes that gate.

• cuVS wheels are Linux-only (cuvs-cu13 with CUDA 13 and cupy-cuda13x). nornir:dev and nornir:cupy install that pair. Other platforms keep the SciPy nearest-neighbor path. The public API does not change.

• The host still needs a matching NVIDIA driver and GPU passthrough. Compute capability 7.5 or newer is required for the CUDA 13 images. See Host requirements.

Copies between host memory and the device dominate many registration steps. Keep a chunk of work on one side until it is finished. A GPU timer that ignores transfer time is not a reason to move a stage.

Mosaic grid refine, as currently structured, can be slower on the GPU than on the CPU because of those copies. The measurement write-up, hardware, and how to reproduce it are in nornir-imageregistration/docs/mosaic_refine_grid_gpu_assessment.md inside a checkout. Read that note before changing the refine backend.

Where the algorithms live, beyond the generated API page: phase correlation, local_distortion_correction (grid refine), and blob_filter.