Knowledge-Based Data Dependence Testing

• Main Authors: Wen-Chung Shih, 時文中
• Other Authors: Shian-Shyong Tseng
• Format: Others
• Language: zh-TW
• Published: 1994
• Online Access: http://ndltd.ncl.edu.tw/handle/50091605830123342212

碩士 === 國立交通大學 === 資訊科學學系 === 82 === More and more popular multiprocessor architectures have made traditional compiling methodology insufficient. For this reason, various parallelizing compiling techniques have been developed to exploit the potential parallelism. Although none of the new methods is suitable for all input cases, each has its own advantages over other ones. This motivates us to study the feasibility of integrating these techniques by knowledge- based approaches. In this thesis, we concentrate on the fundamental phase, data dependence analysis, in parallelizing compilers. We propose a new approach which integrates existing tests and makes good use of their advantages. This approach chooses an appropriate test by knowledge-based methodology, and then applies the resulting test to detect data dependence on loops. A rule-based system, called the K test, is developed by repertory grid analysis to construct the knowledge base. Simulation results show that the K test gives relatively exact solutions in both practical and contrived cases; furthermore, as for system maintenance and extendibility, our approach is obviously superior to others. Therefore, we are trying to extend the knowledge-based approach to the whole field of parallelizing compiling.