Authors:
Amila De Silva
and
Shehan Perera
Affiliation:
Department of Computer Science & Engineering, University of Moratuwa, Katubedda and Sri Lanka
Keyword(s):
Data Mining, Classification, Bitmaps, Bit-Slices, GPU.
Related
Ontology
Subjects/Areas/Topics:
Architectural Concepts
;
Artificial Intelligence
;
Biomedical Engineering
;
Business Analytics
;
Data Analytics
;
Data Engineering
;
Data Management and Quality
;
Data Mining
;
Database Architecture and Performance
;
Databases and Data Security
;
Databases and Information Systems Integration
;
Datamining
;
Enterprise Information Systems
;
Health Information Systems
;
Knowledge Discovery and Information Retrieval
;
Knowledge-Based Systems
;
Sensor Networks
;
Signal Processing
;
Soft Computing
;
Symbolic Systems
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.