A hybrid two-level parallel paradigm with MPI/OpenMP is presented in the context of high-order methods and implemented in the spectral/hp element framework to take advantage of the hierarchical structures arising from deterministic and stochastic CFD problems. We take a coarse grain approach to OpenMP shared-memory parallelization and employ a workload-splitting scheme that reduces the OpenMP synchronizations to the minimum. The hybrid algorithm shows good scalability with respect to both the problem size and the number of processors for a fixed problem size. For the same number of processors, the hybrid model with 2 OpenMP threads per MPI process is observed to perform better than pure MPI and pure OpenMP on the SGI Origin 2000 and the Intel IA64 Cluster, while the pure MPI model performs the best on the IBM SP3 and on the Compaq Alpha Cluster. A key new result is that the use of threads facilitates effectively p-refinement, which is crucial to adaptive discretization using high-order methods.