Multi– GPU Implementation of Machine Learning Algorithm using CUDA and OpenCL
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F01962001%3A_____%2F16%3AN0000002" target="_blank" >RIV/01962001:_____/16:N0000002 - isvavai.cz</a>
Alternative codes found
RIV/00216305:26220/16:PU119308
Result on the web
<a href="http://ijates.org/index.php/ijates/article/view/142" target="_blank" >http://ijates.org/index.php/ijates/article/view/142</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/http://dx.doi.org/10.11601/ijates.v5i2.142" target="_blank" >http://dx.doi.org/10.11601/ijates.v5i2.142</a>
Alternative languages
Result language
angličtina
Original language name
Multi– GPU Implementation of Machine Learning Algorithm using CUDA and OpenCL
Original language description
Using modern Graphic Processing Units (GPUs) becomes very useful for computing complex and time consuming processes. GPUs provide high–performance computation capabilities with a good price. This paper deals with a multi–GPU OpenCL and CUDA implementations of k–Nearest Neighbor (k– NN) algorithm. This work compares performances of OpenCL and CUDA implementations where each of them is suitable for different number of used attributes. The proposed CUDA algorithm achieves acceleration up to 880x 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.
Czech name
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Czech description
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Classification
Type
J<sub>x</sub> - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)
CEP classification
IN - Informatics
OECD FORD branch
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Result continuities
Project
<a href="/en/project/TH01010277" target="_blank" >TH01010277: BriskMiner - Effective tool for advanced analytics of business processes</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2016
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
International Journal of Advances in Telecommunications, Electrotechnics, Signals and Systems
ISSN
1805-5443
e-ISSN
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Volume of the periodical
5
Issue of the periodical within the volume
2
Country of publishing house
CZ - CZECH REPUBLIC
Number of pages
7
Pages from-to
101-107
UT code for WoS article
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EID of the result in the Scopus database
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