The K Test : an Exact and Efficient Knowledge-based Data Dependence Testing Method for Parallelizing Compilers

Many different classes of multiprocessors have been designed and implemented in industry and academia. Therefore, it has become an important issue to develop parallelizing compiling techniques that can exploit the potential power of multiprocessors. In this paper, we concentrate on the fundamental phase, data dependence analysis, in parallelizing compilers. We propose a new approach that integrates existing tests and makes good use of their advantages. This approach chooses an appropriate test using knowledge-based techniques, and then applies the resulting test to detect data dependences on loops. A rule-based system, called the K test, is developed using repertory grid analysis to construct the knowledge base. Simulation results show that the K test gives exact solutions in most of practical and contrived cases; furthermore, 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.