Multi-GPU Implementation of k-Nearest Neighbor Algorithm

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F14%3APU108815" target="_blank" >RIV/00216305:26220/14:PU108815 - isvavai.cz</a>

Výsledek na webu

<a href="https://ieeexplore.ieee.org/document/7296368" target="_blank" >https://ieeexplore.ieee.org/document/7296368</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/TSP.2015.7296368" target="_blank" >10.1109/TSP.2015.7296368</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Multi-GPU Implementation of k-Nearest Neighbor Algorithm

Popis výsledku v původním jazyce

Using modern Graphic Processing Units (Gills) becomes very useful for computing complex and time consuming processes. CPUs provide high performance computation capabilities with a good price. This paper deals with a multi-GPU OpenCL implementation of k-Nearest Neighbor (k-NN) algorithm. The proposed OpenCL algorithm achieves acceleration up to 750x in comparison with a single thread CPU version. The common k-NN was modified to be faster when the lower number of k neighbors is set. The performance of algorithm was verified with two GPUs dual-core NVIDIA GeForce GTX 690 and CPU Intel Core i7 3770 with 4.1 GHz frequency. The results of speed up were measured for one GPU, two GPUs, three and four GPUs. We performed several tests with data sets containing up to 4 million elements with various number of attributes.

Název v anglickém jazyce

Multi-GPU Implementation of k-Nearest Neighbor Algorithm

Popis výsledku anglicky

Using modern Graphic Processing Units (Gills) becomes very useful for computing complex and time consuming processes. CPUs provide high performance computation capabilities with a good price. This paper deals with a multi-GPU OpenCL implementation of k-Nearest Neighbor (k-NN) algorithm. The proposed OpenCL algorithm achieves acceleration up to 750x in comparison with a single thread CPU version. The common k-NN was modified to be faster when the lower number of k neighbors is set. The performance of algorithm was verified with two GPUs dual-core NVIDIA GeForce GTX 690 and CPU Intel Core i7 3770 with 4.1 GHz frequency. The results of speed up were measured for one GPU, two GPUs, three and four GPUs. We performed several tests with data sets containing up to 4 million elements with various number of attributes.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

20201 - Electrical and electronic engineering

Projekt

<a href="/cs/project/FR-TI4%2F151" target="_blank" >FR-TI4/151: Výzkum a vývoj technologie pro detekci emocí v nestrukturovaných datech</a><br>

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2015

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Proceedings of the 38th International Conference on Telecommunication and Signal Processing

ISBN

978-1-4799-8497-8

ISSN

—

e-ISSN

—

Počet stran výsledku

4

Strana od-do

764-767

Název nakladatele

Neuveden

Místo vydání

Berlin, Germany

Místo konání akce

Berlín

Datum konání akce

1. 7. 2014

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000375231000259