Scaling Support Vector Machines On Modern HPC Platforms

Journal Article · Sun Feb 01 00:00:00 EST 2015 · Journal of Parallel and Distributed Computing, 76:16-31

DOI:https://doi.org/10.1016/j.jpdc.2014.09.005· OSTI ID:1184976

You, Yang; Fu, Haohuan; Song, Shuaiwen; Randles, Amanda; Kerbyson, Darren J.; Marquez, Andres; Yang, Guangwen; Hoisie, Adolfy

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.

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