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Title: MIC-SVM: Designing A Highly Efficient Support Vector Machine For Advanced Modern Multi-Core and Many-Core Architectures

Abstract

Support Vector Machine (SVM) has been widely used in data-mining and Big Data applications as modern commercial databases start to attach an increasing importance to the analytic capabilities. In recent years, SVM was adapted to the field of High Performance Computing for power/performance prediction, auto-tuning, and runtime scheduling. However, even at the risk of losing prediction accuracy due to insufficient runtime information, researchers can only afford to apply offline model training to avoid significant runtime training overhead. To address the challenges above, we designed and implemented MICSVM, a highly efficient parallel SVM for x86 based multi-core and many core architectures, such as the Intel Ivy Bridge CPUs and Intel Xeon Phi coprocessor (MIC).

Authors:
; ; ; ; ; ; ; ;
Publication Date:
Research Org.:
Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1158498
Report Number(s):
PNNL-SA-102817
KJ0402000
DOE Contract Number:  
AC05-76RL01830
Resource Type:
Conference
Resource Relation:
Conference: IEEE 28th International Parallel and Distributed Processing Symposium (IPDPS 2014), May 19-23, 2014, Phoenix, Arizona, 809-818
Country of Publication:
United States
Language:
English
Subject:
42 ENGINEERING; Parallel Architectures; Modeling techniques; Machine Learning Mathod; Runtime System

Citation Formats

You, Yang, Song, Shuaiwen, Fu, Haohuan, Marquez, Andres, Mehri Dehanavi, Maryam, Barker, Kevin J., Cameron, Kirk, Randles, Amanda, and Yang, Guangwen. MIC-SVM: Designing A Highly Efficient Support Vector Machine For Advanced Modern Multi-Core and Many-Core Architectures. United States: N. p., 2014. Web. doi:10.1109/IPDPS.2014.88.
You, Yang, Song, Shuaiwen, Fu, Haohuan, Marquez, Andres, Mehri Dehanavi, Maryam, Barker, Kevin J., Cameron, Kirk, Randles, Amanda, & Yang, Guangwen. MIC-SVM: Designing A Highly Efficient Support Vector Machine For Advanced Modern Multi-Core and Many-Core Architectures. United States. doi:10.1109/IPDPS.2014.88.
You, Yang, Song, Shuaiwen, Fu, Haohuan, Marquez, Andres, Mehri Dehanavi, Maryam, Barker, Kevin J., Cameron, Kirk, Randles, Amanda, and Yang, Guangwen. Sat . "MIC-SVM: Designing A Highly Efficient Support Vector Machine For Advanced Modern Multi-Core and Many-Core Architectures". United States. doi:10.1109/IPDPS.2014.88.
@article{osti_1158498,
title = {MIC-SVM: Designing A Highly Efficient Support Vector Machine For Advanced Modern Multi-Core and Many-Core Architectures},
author = {You, Yang and Song, Shuaiwen and Fu, Haohuan and Marquez, Andres and Mehri Dehanavi, Maryam and Barker, Kevin J. and Cameron, Kirk and Randles, Amanda and Yang, Guangwen},
abstractNote = {Support Vector Machine (SVM) has been widely used in data-mining and Big Data applications as modern commercial databases start to attach an increasing importance to the analytic capabilities. In recent years, SVM was adapted to the field of High Performance Computing for power/performance prediction, auto-tuning, and runtime scheduling. However, even at the risk of losing prediction accuracy due to insufficient runtime information, researchers can only afford to apply offline model training to avoid significant runtime training overhead. To address the challenges above, we designed and implemented MICSVM, a highly efficient parallel SVM for x86 based multi-core and many core architectures, such as the Intel Ivy Bridge CPUs and Intel Xeon Phi coprocessor (MIC).},
doi = {10.1109/IPDPS.2014.88},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Sat Aug 16 00:00:00 EDT 2014},
month = {Sat Aug 16 00:00:00 EDT 2014}
}

Conference:
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