Stochastic gradient descent‐based support vector machines training optimization on Big Data and HPC frameworks. (30th March 2021)

• Record Type: Journal Article
• Authors: Abeykoon, Vibhatha; Fox, Geoffrey; Kim, Minje; Ekanayake, Saliya; Kamburugamuve, Supun; Govindarajan, Kannan; Wickramasinghe, Pulasthi; Perera, Niranda; Widanage, Chathura; Uyar, Ahmet; Gunduz, Gurhan; Akkas, Selahatin
• Is Part Of: Concurrency and computation. Volume 34:Number 8(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-03-30
• Subjects: dataflow -- high‐performance computing -- hybrid systems -- machine learning -- SVM; Parallel processing (Electronic computers) -- Periodicals; Parallel computers -- Periodicals; 004.35
• DOI: 10.1002/cpe.6292
• Languages: English
• ISSNs: 1532-0626

Abstract

Summary: Support vector machines (SVM) is a widely used machine learning algorithm. With the increasing amount of research data nowadays, understanding how to do efficient training is more important than ever. This article discusses the performance optimizations and benchmarks related to providing high‐performance support for SVM training. In this research, we have focused on a highly scalable gradient descent‐based approach to implementing the core SVM algorithm. In providing a scalable solution, we have designed optimized high‐performance computing and dataflow‐oriented SVM implementations. A high‐performance computing approach means the algorithm is implemented with the bulk synchronous parallel (BSP) model. In addition, we analyzed the language level optimizations and math kernel optimizations on a prominent HPC modeling programming language (C++) and dataflow modeling programming language (Java). In the experiments, we compared the performance of classic HPC models, classic dataflow models, and hybrid models designed on classic HPC and dataflow programming models. Our research illustrates a scientific approach in designing the SVM algorithm at scale in classic HPC, dataflow, and hybrid systems.