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Parallel Computing of Support Vector Machines: A Survey
University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre. University of Borås. (Skövde Artificial Intelligence Lab (SAIL))ORCID iD: 0000-0003-0669-9978
2019 (English)In: ACM Computing Surveys, ISSN 0360-0300, E-ISSN 1557-7341, Vol. 51, no 6, p. 123:1-123:38, article id 123Article, review/survey (Refereed) Published
Abstract [en]

The immense amount of data created by digitalization requires parallel computing for machine-learning methods. While there are many parallel implementations for support vector machines (SVMs), there is no clear suggestion for every application scenario. Many factor—including optimization algorithm, problem size and dimension, kernel function, parallel programming stack, and hardware architecture—impact the efficiency of implementations. It is up to the user to balance trade-offs, particularly between computation time and classification accuracy. In this survey, we review the state-of-the-art implementations of SVMs, their pros and cons, and suggest possible avenues for future research.

Place, publisher, year, edition, pages
Association for Computing Machinery (ACM), 2019. Vol. 51, no 6, p. 123:1-123:38, article id 123
Keywords [en]
Dual optimization, primal optimization, decomposition, CPU parallelism, GPU parallelism, speedup, data movement
National Category
Engineering and Technology Computer Sciences Computer and Information Sciences Information Systems
Research subject
INF301 Data Science; Skövde Artificial Intelligence Lab (SAIL)
Identifiers
URN: urn:nbn:se:his:diva-16554DOI: 10.1145/3280989ISI: 000460376100014Scopus ID: 2-s2.0-85061196907OAI: oai:DiVA.org:his-16554DiVA, id: diva2:1278476
Funder
Knowledge FoundationAvailable from: 2019-01-14 Created: 2019-01-14 Last updated: 2019-07-10Bibliographically approved
In thesis
1. High-Performance Computing For Support Vector Machines
Open this publication in new window or tab >>High-Performance Computing For Support Vector Machines
2018 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

Machine learning algorithms are very successful in solving classification and regression problems, however the immense amount of data created by digitalization slows down the training and predicting processes, if solvable at all. High-Performance Computing(HPC) and particularly parallel computing are promising tools for improving the performance of machine learning algorithms in terms of time. Support Vector Machines(SVM) is one of the most popular supervised machine learning techniques that enjoy the advancement of HPC to overcome the problems regarding big data, however, efficient parallel implementations of SVM is a complex endeavour. While there are many parallel techniques to facilitate the performance of SVM, there is no clear roadmap for every application scenario. This thesis is based on a collection of publications. It addresses the problems regarding parallel implementations of SVM through four research questions, all of which are answered through three research articles. In the first research question, the thesis investigates important factors such as parallel algorithms, HPC tools, and heuristics on the efficiency of parallel SVM implementation. This leads to identifying the state of the art parallel implementations of SVMs, their pros and cons, and suggests possible avenues for future research. It is up to the user to create a balance between the computation time and the classification accuracy. In the second research question, the thesis explores the impact of changes in problem size, and the value of corresponding SVM parameters that lead to significant performance. This leads to addressing the impact of the problem size on the optimal choice of important parameters. Besides, the thesis shows the existence of a threshold between the number of cores and the training time. In the third research question, the thesis investigates the impact of the network topology on the performance of a network-based SVM. This leads to three key contributions. The first contribution is to show how much the expansion property of the network impact the convergence. The next is to show which network topology is preferable to efficiently use the computing powers. Third is to supply an implementation making the theoretical advances practically available. The results show that graphs with large spectral gaps and higher degrees exhibit accelerated convergence. In the last research question, the thesis combines all contributions in the articles and offers recommendations towards implementing an efficient framework for SVMs regarding large-scale problems.

Place, publisher, year, edition, pages
Skövde: University of Skövde, 2018. p. 115
Series
Dissertation Series ; 26 (2018)
National Category
Computer Sciences
Research subject
Skövde Artificial Intelligence Lab (SAIL); INF301 Data Science
Identifiers
urn:nbn:se:his:diva-16556 (URN)978-91-984187-8-1 (ISBN)
Presentation
2019-01-28, G207, Skövde, 13:15 (English)
Opponent
Supervisors
Available from: 2019-01-22 Created: 2019-01-14 Last updated: 2019-02-14Bibliographically approved

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Tavara, Shirin

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