High-performance attribute reduction on graphics processing unit

ABSTRACT

Recently, graphics processing unit (GPU) gained lots of attention from academia and industry for its applicability in high-performance computing. It has been successfully applied to many fields, such as image processing, machine learning, object detection, etc. In our previous work, GPU was adopted to accelerate the computation of rough set approximation (RSA), which is the core step in most of the rough sets based tasks, e.g. attribute reduction. The method is essentially a CPU-GPU cooperative paradigm. That is to say, there are lots of data exchanged between host memory and GPU memory, which greatly degrades the performance of the system. This paper introduces a unified GPU framework for parallel attribute reduction, in which two critical steps in attribute reduction, i.e. computation of equivalence class and attributes significance, are both executed on GPU. Moreover, the algorithm is well designed by exploiting the architectural characteristics of the modern GPU architecture. Experiments were carried out on data sets with different sizes. The results show that the proposed algorithm can outperform the CPU-GPU cooperative algorithm on large data sets.

KEYWORDS:

• Parallel attribute reduction
• rough sets
• GPU
• big data mining

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work is supported by the Project of Science and Technology Bureau of Leshan (Grand No. 18JZD091, 18JZD117); the Scientific Research Fund of Leshan Normal University (Grand No. ZZ201822).