In this paper, we propose a multi-paradigm and multi-grain parallel execution model based on SMPCluster, which integrates coarse grain, mid grain and fine grain parallelism. Multiple paradigms supported by our model include task parallel, data parallel, sequential execution, data pipeline and task-farming paradigm. It can be achieved by extending the OpenMP specification, and the extensions include directives for computing resource partition, data distribution and alignment, sequential execution and data pipeline, and functions for Master/Slave model in Macro-Task group. We also compare the performance of different implementations of three benchmark applications, using the same numerical algorithm but employing different programming approaches.