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dc.contributor.author김상욱-
dc.date.accessioned2019-11-26T05:33:09Z-
dc.date.available2019-11-26T05:33:09Z-
dc.date.issued2017-06-
dc.identifier.citationCLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS, v. 20, no. 2, page. 1155-1166, Special no. SIen_US
dc.identifier.issn1386-7857-
dc.identifier.issn1573-7543-
dc.identifier.urihttps://link.springer.com/article/10.1007%2Fs10586-017-0789-4-
dc.identifier.urihttps://repository.hanyang.ac.kr/handle/20.500.11754/114599-
dc.description.abstractAn intuitive way to process the big data efficiently is to reduce the volume of data transferred over the storage interface to a host system. This is the reason that the notion of intelligent SSD (iSSD) was proposed to give processing power to SSD. There is rich literature on iSSD, however, its real implementation has not been provided to the public yet. Most prior work aims to quantify the benefits of iSSD with analytical modeling. In this paper, we first develop on iSSD simulator and present the potential of iSSD in data mining through the iSSD simulator. Our iSSD simulator performs on top of the gem 5 simulator and fully simulates all the processes of data mining algorithms running in iSSD with cycle-level accuracy. Then, we further addresse how to exploit all the computing resources for efficient processing of data mining algorithms. These days, CPU, GPU, and SSD are recently equipped together in most computing environment. If SSD is replaced with iSSD later on, we have a new computing environment where the three computing resources collaborate one another to process big data quite effectively. For this, scheduling is required to decide which computing resource is going to run for which function at which time. In our heterogeneous scheduling, types of computing resources, memory sizes in computing resources, and inter-processor communication times including IO time in SSD are considered. Our scheduling results show that processing in the collaborative environment outperforms that in the traditional one by up to about 10 times.en_US
dc.description.sponsorshipMoonjun Chung helped the implementation of iSSD simulator based on the gem 5 simulator for our experiments. This work was supported by (1) a Semiconductor Industry Collaborative Project between Hanyang University and Samsung Electronics Co. Ltd. and (2) the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIP) (NRF-2014R1A2A1A10054151).en_US
dc.language.isoen_USen_US
dc.publisherSPRINGERen_US
dc.subjectIntelligent SSDen_US
dc.subjectSimulator-based evaluationen_US
dc.subjectCollaborative processingen_US
dc.subjectHeterogeneous schedulingen_US
dc.titleHigh-performance data mining with intelligent SSDen_US
dc.typeArticleen_US
dc.relation.volume20-
dc.identifier.doi10.1007/s10586-017-0789-4-
dc.relation.page1155-1166-
dc.relation.journalCLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS-
dc.contributor.googleauthorJo, Yong-Yeon-
dc.contributor.googleauthorKim, Sang-Wook-
dc.contributor.googleauthorCho, Sung-Woo-
dc.contributor.googleauthorBae, Duck-Ho-
dc.contributor.googleauthorOh, Hyunok-
dc.relation.code2017010478-
dc.sector.campusS-
dc.sector.daehakCOLLEGE OF ENGINEERING[S]-
dc.sector.departmentDEPARTMENT OF COMPUTER SCIENCE-
dc.identifier.pidwook-
Appears in Collections:
COLLEGE OF ENGINEERING[S](공과대학) > COMPUTER SCIENCE(컴퓨터소프트웨어학부) > Articles
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