An Auto-Matching Model with Pattern Recognition Using Bayesian Classifier for Parallel Programming on A Multi-Core Processor

Abstract

The emerging multi-core processor architecture has greatly escalated scientific computing, but, at the same time, made parallel programming increasingly complex and challenging. In this paper, the use of the Auto Parallel Classification (APC) model in an Object-Oriented Parallel Model (OOPModel) environment is demonstrated. A designed module provides a traversal and a reduction of the DAG task graph. The parallel characteristics vectors, which are analyzed according to Naive Bayesian classification theory, are critical parameters for matching and generating parallel design patterns and various skeletal frameworks. Through extensive experimentation, it is demonstrated, that by using the Map-Reduce pattern to develop a minimum-sort algorithm, in conjunction with the APC model, we can achieve a reduction in the complexity of parallel programming and the minimization of errors. Most importantly, through scientific experimentation, this document will further demonstrate that correct computational results and movements toward linear speed-up can be accomplished.