关键词: 人工神经网络, 基本块重排, 子结构分析, 编译优化

Abstract: The authors present a basic-block reordering method that detects typical structures in the control-flow graph. It utilizes the architecture-specific branch cost model and execution possibilities of control-flow edges to estimate the possible layout costs of specific sub-structures. The layout with the minimal cost estimation would be chosen. The authors further investigate a novel approach to apply neural network to predict execution possibility for each edge. A set of programs are chosen to record particular static information of the edges in the typical structures. The data include the knowledge about the relationship between static program features and dynamic behaviors. It is fed to train an improved back propagation neural network (RPROP). The algorithm is implemented based on a simple pipeline UniCore microprocessor. Experiment result shows that it improves programs?performance about 8% , which indicates that the execution possibility of edges may be predicted using machine learning techniques.

Key words: artificial neural network, basic-block reordering, structural analysis, compiler optimization