Nearest Neighbor in OpenCL

I'd like to start this discussion because I might research this topic when I get bored. I am not an OpenCL or CUDA expert (or even novice) so I'm putting my findings here as I go.

• https://forums.khronos.org/showthread.php/7402-kd-tree-nearest-neighbour
  Summary: KDTree is not typically a great algorithm for GPUs, it is intentionally good for CPUs. At the end someone provides a link to their paper on a GPU-friendly KDTree algorithm.
• https://github.com/gieseke/bufferkdtree
  Python library with GPU OpenCL implementation with a mix of a KDTree and brute force algorithm.
• HALMD: http://halmd.org/
  C++ library advertised on Stackoverflow as "the fastest nearest neighbor algorithm on a GPU"
• https://stackoverflow.com/questions/5381397/openclnearest-neighbour-using-euclidean-distance
  Simple OpenCL code for a brute force "minimum distance between this point and all others"
• https://arxiv.org/abs/0804.1448
  Fast k Nearest Neighbor Search using GPU
• https://github.com/Jackie-Chou/pyopencl_knn
• https://github.com/yszheda/OpenCL-knn-1GPU
• https://github.com/chrischoy/knn_cuda
  Has a python wrapper and has lots of stars

The issue names no repository files, entry points, or tests. Start by reviewing the linked OpenCL and GPU nearest-neighbor resources, including bufferkdtree, pyopencl_knn, and the cited papers. A concrete project scope and completion criteria would need to be established before implementation can be considered done.