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dc.contributor.authorRajani, Nazneen
dc.contributor.authorMcArdle, Kate
dc.contributor.authorDhillon, Inderjit S.
dc.identifier.citationRajani, Nazneen; McArdle, Kate; Dhillon, Inderjit S. Parallel k nearest neighbor graph construction using tree-based data structures. A: HPGM: High Performance Graph Mining. "1st High Performance Graph Mining workshop, Sydney, 10 August 2015". 2015.
dc.description.abstractConstruction of a nearest neighbor graph is often a neces- sary step in many machine learning applications. However, constructing such a graph is computationally expensive, es- pecially when the data is high dimensional. Python's open source machine learning library Scikit-learn uses k-d trees and ball trees to implement nearest neighbor graph construc- tion. However, this implementation is ine cient for large datasets. In this work, we focus on exploiting these under- lying tree-based data structures to optimize parallel execu- tion of the nearest neighbor algorithm. We present parallel implementations of nearest neighbor graph construction us- ing such tree structures, with parallelism provided by the OpenMP and the Galois framework. We empirically show that our parallel and exact approach is e cient as well as scalable, compared to the Scikit-learn implementation. We present the rst implementation of k-d trees and ball trees using Galois. Our results show that k-d trees are faster when the number of dimensions is small (2d N); ball trees on the other hand scale well with the number of dimensions. Our implementation of ball trees in Galois has almost linear speedup on a number of datasets irrespective of the size and dimensionality of the data.
dc.format.extent8 p.
dc.relation.ispartofHigh Performance Graph Mining workshop (1st: 2015: Sydney)
dc.rightsAttribution-ShareAlike 3.0 Spain
dc.subjectÀrees temàtiques de la UPC::Matemàtiques i estadística
dc.titleParallel k nearest neighbor graph construction using tree-based data structures
dc.typeConference report
dc.rights.accessOpen Access
upcommons.citation.contributorHPGM: High Performance Graph Mining
upcommons.citation.publicationName1st High Performance Graph Mining workshop, Sydney, 10 August 2015

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