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dc.contributor.author윤동원-
dc.date.accessioned2016-11-09T06:31:04Z-
dc.date.available2016-11-09T06:31:04Z-
dc.date.issued2015-04-
dc.identifier.citationInt'l Conference on Computer Science, Data Mining & Mechanical Engg. (ICCDMME’2015) April 20-21, 2015 Bangkok (Thailand), Page. 125-129en_US
dc.identifier.urihttp://iieng.org/images/proceedings_pdf/9288E0415070.pdf-
dc.identifier.urihttp://hdl.handle.net/20.500.11754/24262-
dc.description.abstractToday, there is no one who disagrees on how important data is in every industry especially in enterprise market. More recently, the key point that decides the survival of a business is the management of their big data, which is defined by the 3V‟s: Volume, Velocity, and Variety [1]. While the rate of data generation increases exponentially, processing that data with the limited resources can be a burden to the both business managers and IT managers. Therefore many researchers have already studied new systems which can serve as an alternative resource to calculate and process data. Parallel hardware, such as a general-purpose GPU (GPGPU), is one of the most well-known alternative. With them, it is possible to process various applications, including data-intensive applications, quickly [2]. OpenCL, in collaborated with several GPU vendors and software organizations, has been launched by the Khronos group as the first open standard platform for the programming of both the GPUs and CPUs [3]. It makes the binary codes execute on various heterogeneous processing units such as CPUs, GPUs and FPGAs simultaneously. It also supports small clients like mobile GPUs for the mobile world. This paper proposes the method to optimize a machine learning algorithm with the heterogeneous platform which uses both the CPUs and GPUs using OpenCL. Through the experiment, we show that our method can reduce the execution time of the k-means nearest clustering algorithm, which is one of the most common algorithms in the machine learning industry, up to 40%. The more data we use in our system, the faster our results are when compared to the experiment in the multi-core system.en_US
dc.description.sponsorshipThis work was partially supported by the MSIP, Korea, under the CITRC support program (NIPA -2014 -H0401 -14-1001) supervised by the NIPA, and the NRF grant funded by the Korea government (MSIP) (NRF-2014R1A2A1A11052701).en_US
dc.language.isoenen_US
dc.publisherIIEen_US
dc.subjectMachine Learningen_US
dc.subjectk-means algorithmen_US
dc.subjectOpenCLen_US
dc.subjectHeterogeneous computingen_US
dc.titleOptimization of a machine learning algorithm on the Heterogeneous system using OpenCLen_US
dc.typeArticleen_US
dc.relation.page122-126-
dc.contributor.googleauthorSong, Min Gyung-
dc.contributor.googleauthorYoon, Dongweon-
dc.sector.campusS-
dc.sector.daehakCOLLEGE OF ENGINEERING[S]-
dc.sector.departmentDEPARTMENT OF ELECTRONIC ENGINEERING-
dc.identifier.piddwyoon-
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COLLEGE OF ENGINEERING[S](공과대학) > ELECTRONIC ENGINEERING(융합전자공학부) > Articles
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