Project details for bufferkdtree

Screenshot bufferkdtree 1.3

by fgieseke - October 20, 2017, 11:39:59 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ]

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Description:

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).

Changes to previous version:

Initial Announcement on mloss.org.

BibTeX Entry: Download
Corresponding Paper BibTeX Entry: Download
URL: Project Homepage
Supported Operating Systems: Linux
Data Formats: Numpy, Python
Tags: Large Scale, Nearest Neighbors, Gpu, High Performance Computing, Big Data
Archive: download here

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