bufferkdtree: a Python library for massive nearest neighbor queries on multi-many-core devices

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Standard

bufferkdtree : a Python library for massive nearest neighbor queries on multi-many-core devices. / Gieseke, Fabian Cristian; Oancea, Cosmin Eugen; Igel, Christian.

I: Knowledge-Based Systems, Bind 120, 15.03.2017, s. 1-3.

Publikation: Bidrag til tidsskriftTidsskriftartikelForskning

Harvard

Gieseke, FC, Oancea, CE & Igel, C 2017, 'bufferkdtree: a Python library for massive nearest neighbor queries on multi-many-core devices', Knowledge-Based Systems, bind 120, s. 1-3. https://doi.org/10.1016/j.knosys.2017.01.002

APA

Gieseke, F. C., Oancea, C. E., & Igel, C. (2017). bufferkdtree: a Python library for massive nearest neighbor queries on multi-many-core devices. Knowledge-Based Systems, 120, 1-3. https://doi.org/10.1016/j.knosys.2017.01.002

Vancouver

Gieseke FC, Oancea CE, Igel C. bufferkdtree: a Python library for massive nearest neighbor queries on multi-many-core devices. Knowledge-Based Systems. 2017 mar 15;120:1-3. https://doi.org/10.1016/j.knosys.2017.01.002

Author

Gieseke, Fabian Cristian ; Oancea, Cosmin Eugen ; Igel, Christian. / bufferkdtree : a Python library for massive nearest neighbor queries on multi-many-core devices. I: Knowledge-Based Systems. 2017 ; Bind 120. s. 1-3.

Bibtex

@article{c604f21d08ae4d3294f007221c8202ba,
title = "bufferkdtree: a Python library for massive nearest neighbor queries on multi-many-core devices",
abstract = "The bufferkdtree package is an open-source software that provides an efficient implementation for processing huge amounts of nearest neighbor queries in Euclidean spaces of moderate dimensionality. Its underlying implementation resorts to a variant of the classical k-d tree data structure, called buffer k-d tree, which can be used to efficiently perform bulk nearest neighbor searches on modern many-core devices. The package, which is based on Python, C, and OpenCL, is made publicly available online at https://github.com/gieseke/bufferkdtree under the GPLv2 license.",
keywords = "GPUs, k-d trees, Nearest neighbor queries, OpenCL, Python",
author = "Gieseke, {Fabian Cristian} and Oancea, {Cosmin Eugen} and Christian Igel",
year = "2017",
month = "3",
day = "15",
doi = "10.1016/j.knosys.2017.01.002",
language = "English",
volume = "120",
pages = "1--3",
journal = "Knowledge-Based Systems",
issn = "0950-7051",
publisher = "Elsevier",

}

RIS

TY - JOUR

T1 - bufferkdtree

T2 - a Python library for massive nearest neighbor queries on multi-many-core devices

AU - Gieseke, Fabian Cristian

AU - Oancea, Cosmin Eugen

AU - Igel, Christian

PY - 2017/3/15

Y1 - 2017/3/15

N2 - The bufferkdtree package is an open-source software that provides an efficient implementation for processing huge amounts of nearest neighbor queries in Euclidean spaces of moderate dimensionality. Its underlying implementation resorts to a variant of the classical k-d tree data structure, called buffer k-d tree, which can be used to efficiently perform bulk nearest neighbor searches on modern many-core devices. The package, which is based on Python, C, and OpenCL, is made publicly available online at https://github.com/gieseke/bufferkdtree under the GPLv2 license.

AB - The bufferkdtree package is an open-source software that provides an efficient implementation for processing huge amounts of nearest neighbor queries in Euclidean spaces of moderate dimensionality. Its underlying implementation resorts to a variant of the classical k-d tree data structure, called buffer k-d tree, which can be used to efficiently perform bulk nearest neighbor searches on modern many-core devices. The package, which is based on Python, C, and OpenCL, is made publicly available online at https://github.com/gieseke/bufferkdtree under the GPLv2 license.

KW - GPUs

KW - k-d trees

KW - Nearest neighbor queries

KW - OpenCL

KW - Python

UR - http://www.scopus.com/inward/record.url?scp=85008472806&partnerID=8YFLogxK

U2 - 10.1016/j.knosys.2017.01.002

DO - 10.1016/j.knosys.2017.01.002

M3 - Journal article

AN - SCOPUS:85008472806

VL - 120

SP - 1

EP - 3

JO - Knowledge-Based Systems

JF - Knowledge-Based Systems

SN - 0950-7051

ER -

ID: 179272724