MIC-SVM: Designing a highly efficient support vector machine for advanced modern multi-core and many-core architectures

Proceedings of the International Parallel and Distributed Processing Symposium, IPDPS

Volumn , Issue , 2014, Pages 809-818

  You, Yang ^a   Song, Shuaiwen Leon ^b   Fu, Haohuan ^a   Marquez, Andres ^b   Dehnavi, Maryam Mehri ^c   Barker, Kevin ^a   Cameron, Kirk W ^d   Randles, Amanda Peters ^e   Yang, Guangwen ^a  

^a TSINGHUA UNIVERSITY   (China)

^b PACIFIC NORTHWEST NATIONAL LABORATORY   (United States)

^c MASSACHUSETTS INSTITUTE OF TECHNOLOGY   (United States)

^d Virignia Tech   (United States)

^e LAWRENCE LIVERMORE NATIONAL LABORATORY   (United States)

Author keywords

Multi amp; Many Core architectures; Optimization Techniques; Parallelization; Performance Analysis; Support Vector Machine

Indexed keywords

ARTIFICIAL INTELLIGENCE; BIG DATA; COMPUTER ARCHITECTURE; DATA MINING; DESIGN; DISTRIBUTED PARAMETER NETWORKS; MICROPROCESSOR CHIPS; PROGRAM PROCESSORS; SCHEDULING;

HIGH PERFORMANCE COMPUTING; MANY-CORE ARCHITECTURE; MULTI CORE AND MANY CORES; MULTI-LEVEL PARALLELISM; OPTIMIZATION TECHNIQUES; PARALLELIZATIONS; PERFORMANCE ANALYSIS; PERFORMANCE COMPARISON;

SUPPORT VECTOR MACHINES;

EID: 84906690144     PISSN: 15302075     EISSN: 23321237     Source Type: Conference Proceeding    
DOI: 10.1109/IPDPS.2014.88     Document Type: Conference Paper

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