Practical approach to access the impact of global variables on program parallelism

Global variables may have a significant impact on preventing programs from automatic parallelism. This paper introduces a practical approach to measure the effect of global variables on program parallelism. First, we conduct static data dependence analysis among program variables and represent such dependencies by a Variable Dependence Graph. Then, we analyze this graph for measuring and identifying which global variables have a significant impact on program parallelism. To evaluate this approach, we conduct experiments on 20 benchmark programs and an industrial application. The experimental results show that half of the studied programs contain large impact variables which may be the cause of preventing programs from parallel execution.