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  • 1.
    Tavara, Shirin
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.
    High-Performance Computing For Support Vector Machines2018Licentiate 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.

  • 2.
    Tavara, Shirin
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre. University of Borås.
    Parallel Computing of Support Vector Machines: A Survey2019In: 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)
    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.

  • 3.
    Tavara, Shirin
    et al.
    Information Technology, University of Borås, Borås, Sweden.
    Schliep, Alexander
    Computer Science and Engineering, University of Gothenburg, Gothenburg, Sweden.
    Effect Of Network Topology On The Performance Of ADMM-based SVMs2018In: Proceedings 2018 30th International Symposium on Computer Architecture and High Performance Computing SBAC-PAD 2018: Lyon, France 24-27 September 2018, IEEE Computer Society, 2018, p. 388-393Conference paper (Other academic)
    Abstract [en]

    Alternating Direction Method Of Multipliers(ADMM) is one of the promising frameworks for training Support Vector Machines (SVMs) on large-scale data in adistributed manner. In a consensus-based ADMM, nodes may only communicate with one-hop neighbors and this may cause slow convergence. In this paper, we investigate the impact of network topology on the convergence speed of ADMM-basedSVMs using expander graphs. In particular, we investigate how much the expansion property of the network influence the convergence and which topology is preferable. Besides, we supply an implementation making these theoretical advances practically available. The results of the experiments show that graphs with large spectral gaps and higher degrees exhibit accelerated convergence.

  • 4.
    Tavara, Shirin
    et al.
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre. Department of Information Technology, University of Borås, Borås, Sweden.
    Sundell, Håkan
    Department of Information Technology, University of Borås, Borås, Sweden.
    Dahlbom, Anders
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.
    Empirical Study of Time Efficiency and Accuracy of Support Vector Machines Using an Improved Version of PSVM2015In: Proceedings of the 2015 International Conference on Parallel and Distributed Processing Techniques and Applications: PDPTA 2015: Volume 1 / [ed] Hamid R. Arabnia, Hiroshi Ishii, Kazuki Joe, Hiroaki Nishikawa, Havaru Shouno, Printed in the United States of America: CSREA Press, 2015, Vol. 1, p. 177-183Conference paper (Refereed)
    Abstract [en]

    We present a significantly improved implementation of a parallel SVM algorithm (PSVM) together with a comprehensive experimental study. Support Vector Machines (SVM) is one of the most well-known machine learning classification techniques. PSVM employs the Interior Point Method, which is a solver used for SVM problems that has a high potential of parallelism. We improve PSVM regarding its structure and memory management for contemporary processor architectures. We perform a number of experiments and study the impact of the reduced column size p and other important parameters as C and gamma on the class-prediction accuracy and training time. The experimental results show that there exists a threshold between the number of computational cores and the training time, and that choosing an appropriate value of p effects the choice of the C and gamma parameters as well as the accuracy.

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