Accelerating Support Vector Machine Learning with GPU-Based MapReduce

With the exploding growth of data, the computational complexity required by learning Support Vector Machine (SVM) lays a heavy burden on real-world applications. To address this issue, parallel computational techniques can be employed such as the Graphics Processing Units (GPUs) and MapReduce model....

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Bibliographic Details
Published in2015 IEEE International Conference on Systems, Man, and Cybernetics pp. 876 - 881
Main Authors Tianyao Sun, Hanli Wang, Yun Shen, Jun Wu
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.10.2015
Subjects
Acceleration
GPU
Graphics processing units
Hadoop
Kernel
MapReduce
Optimization
Parallel processing
Support vector machine
Support vector machines
Training
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Summary:With the exploding growth of data, the computational complexity required by learning Support Vector Machine (SVM) lays a heavy burden on real-world applications. To address this issue, parallel computational techniques can be employed such as the Graphics Processing Units (GPUs) and MapReduce model. As it is well known, GPUs are microprocessors on a multi-core architecture which reveal high performance in mass data parallel computing, and MapReduce allows computational tasks to be divided into a plurality of parts, distributed to various computing nodes and combined on a single node. In this paper, we propose a GPU-based MapReduce framework to accelerate SVM learning by jointly utilizing the parallel computing power of GPU and MapReduce. Extensive experimental results have verified the effectiveness and efficiency of the proposed approach.
DOI:10.1109/SMC.2015.161