New Approach to Speedup Dynamic Program Parallelization Analysis

Abstract

Development of parallel programming tools has become a mainstream research topic and it has produced many useful tools that reduce the burden of parallel programming. The most important aspect of any such tool is the data dependency analysis, which is very complex and computationally intensive. Fully automatic tools have many demerits such as low efficiency and lack of interaction with the user. In this paper, the authors present our tool called EasyPar having unique features to assist the user at the time of program development as opposed most of the other tools that work on completed programs. Performing parallelization analysis at the time of program development has a potential to exploit more parallelization opportunity by interaction with the programmer. Two most important requirements of such a tool are the accuracy and the performance of the analysis. The authors propose the method that uses database to enable quick and efficient dependency analysis. The proposed method utilizes a database to save program information in structured from and using a query based method for data dependency analysis. The database approach has three benefits. First, it makes the incremental parsing easier and faster; second, query-based approach makes the dependency analysis efficient and third it allows the demand driven program analysis possible. The authors have tested their tool on popular benchmark codes, presented the performance results, and compared it with relevant work.