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Title: Scaling Support Vector Machines On Modern HPC Platforms

Abstract

We designed and implemented MIC-SVM, a highly efficient parallel SVM for x86 based multicore and many-core architectures, such as the Intel Ivy Bridge CPUs and Intel Xeon Phi co-processor (MIC). We propose various novel analysis methods and optimization techniques to fully utilize the multilevel parallelism provided by these architectures and serve as general optimization methods for other machine learning tools.

Authors:
; ; ; ; ; ; ;
Publication Date:
Research Org.:
Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1184976
Report Number(s):
PNNL-SA-105673
KJ0402000
DOE Contract Number:  
AC05-76RL01830
Resource Type:
Journal Article
Journal Name:
Journal of Parallel and Distributed Computing, 76:16-31
Additional Journal Information:
Journal Name: Journal of Parallel and Distributed Computing, 76:16-31
Country of Publication:
United States
Language:
English

Citation Formats

You, Yang, Fu, Haohuan, Song, Shuaiwen, Randles, Amanda, Kerbyson, Darren J., Marquez, Andres, Yang, Guangwen, and Hoisie, Adolfy. Scaling Support Vector Machines On Modern HPC Platforms. United States: N. p., 2015. Web. doi:10.1016/j.jpdc.2014.09.005.
You, Yang, Fu, Haohuan, Song, Shuaiwen, Randles, Amanda, Kerbyson, Darren J., Marquez, Andres, Yang, Guangwen, & Hoisie, Adolfy. Scaling Support Vector Machines On Modern HPC Platforms. United States. https://doi.org/10.1016/j.jpdc.2014.09.005
You, Yang, Fu, Haohuan, Song, Shuaiwen, Randles, Amanda, Kerbyson, Darren J., Marquez, Andres, Yang, Guangwen, and Hoisie, Adolfy. 2015. "Scaling Support Vector Machines On Modern HPC Platforms". United States. https://doi.org/10.1016/j.jpdc.2014.09.005.
@article{osti_1184976,
title = {Scaling Support Vector Machines On Modern HPC Platforms},
author = {You, Yang and Fu, Haohuan and Song, Shuaiwen and Randles, Amanda and Kerbyson, Darren J. and Marquez, Andres and Yang, Guangwen and Hoisie, Adolfy},
abstractNote = {We designed and implemented MIC-SVM, a highly efficient parallel SVM for x86 based multicore and many-core architectures, such as the Intel Ivy Bridge CPUs and Intel Xeon Phi co-processor (MIC). We propose various novel analysis methods and optimization techniques to fully utilize the multilevel parallelism provided by these architectures and serve as general optimization methods for other machine learning tools.},
doi = {10.1016/j.jpdc.2014.09.005},
url = {https://www.osti.gov/biblio/1184976}, journal = {Journal of Parallel and Distributed Computing, 76:16-31},
number = ,
volume = ,
place = {United States},
year = {Sun Feb 01 00:00:00 EST 2015},
month = {Sun Feb 01 00:00:00 EST 2015}
}