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Parallel pattern classification utilizing GPU-based kernelized Slackmin algorithm
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DOI：10.1016/j.jpdc.2016.09.001.png)
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Abstract
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This paper introduces a parallel implementation of the kernelized Slackmin algorithm able to tackle medium scale data in pattern classification applications. Initially, the main principles of the serial Slackmin algorithm are described, with emphasis to its parallel nature making its parallelization a straightforward task. The parallelization is achieved by utilizing the parallel processing capabilities of the CUDA architecture of a low cost NVIDIA GPU card. The resulted GPU-based Slackmin algorithm named cuKSlackmin is able to classify medium scale data in a reasonable time without sacrificing its classification performance. A detailed comparison with some established GPU-based classification algorithms, widely used in machine learning, has proved the high performance of the proposed scheme as an alternative tool for medium scale data classification. (C) 2016 Elsevier Inc. All rights reserved.
Keywords:
GPU programming
Machine learning
Big data
Pattern classification
Parallel algorithms
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