A Parallel Bit-map based Framework for Classification Algorithms

Amila De Silva, Shehan Perera

Abstract

Bitmaps are gaining popularity with Data Mining Applications that use GPUs, since Memory organisation and the design of a GPU demands for regular & simple structures. However, absence of a common framework has limited the benefits of Bitmaps & GPUs mostly to Frequent Itemset Mining (FIM) algorithms. We in this paper, present a framework based on Bitmap techniques, that speeds up Classification Algorithms on GPUs. The proposed framework which uses both CPU and GPU for Algorithm execution, delegates compute intensive operations to GPU. We implement two Classification Algorithms Naíve Bayes and Decision Trees, using the framework, both which outperform CPU counterparts by several orders of magnitude.

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Paper Citation


in Harvard Style

De Silva A. and Perera S. (2019). A Parallel Bit-map based Framework for Classification Algorithms.In Proceedings of the 8th International Conference on Data Science, Technology and Applications - Volume 1: DATA, ISBN 978-989-758-377-3, pages 259-266. DOI: 10.5220/0007931202590266


in Bibtex Style

@conference{data19,
author={Amila De Silva and Shehan Perera},
title={A Parallel Bit-map based Framework for Classification Algorithms},
booktitle={Proceedings of the 8th International Conference on Data Science, Technology and Applications - Volume 1: DATA,},
year={2019},
pages={259-266},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007931202590266},
isbn={978-989-758-377-3},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 8th International Conference on Data Science, Technology and Applications - Volume 1: DATA,
TI - A Parallel Bit-map based Framework for Classification Algorithms
SN - 978-989-758-377-3
AU - De Silva A.
AU - Perera S.
PY - 2019
SP - 259
EP - 266
DO - 10.5220/0007931202590266