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The bufferkdtree package is a Python library that aims at accelerating nearest neighbor computations using both k-d trees and modern many-core devices such as graphics processing units (GPUs). The implementation is based on OpenCL.

The buffer k-d tree technique can be seen as an intermediate version between a standard parallel k-d tree traversal (on multi-core systems) and a massively-parallel brute-force implementation for nearest neighbor search. In particular, it makes use of the top of a standard k-d tree (which induces a spatial subdivision of the space) and resorts to a simple yet efficient brute-force implementation for processing chunks of "big" leaves. The implementation is well-suited for data sets with a large reference set (e.g., 1,000,000 points) and a huge query set (e.g., 10,000,000 points) given a moderate dimensionality of the search space (e.g., from d=5 to d=50).


See the documentation for details and examples.


The bufferkdtree package has been tested under Python 2.6/2.7/3.*. The required Python dependencies are:

  • NumPy >= 1.11.0

Further, Swig, OpenCL (version >= 1.2), setuptools, and a working C/C++ compiler need to be available. See the documentation for more details.


The package can easily be installed via pip via:

pip install bufferkdtree

To install the package from the sources, first get the current stable release via:

git clone

Afterwards, on Linux systems, you can install the package locally for the current user via:

python install --user

On Debian/Ubuntu systems, the package can be installed globally for all users via:

python build
sudo python install


The source code is published under the GNU General Public License (GPLv2). The authors are not responsible for any implications that stem from the use of this software.


A Python library for large-scale nearest neigbhor computations via k-d trees and GPUs.







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