Abstract
A performance-prediction model is presented, which describes different hierarchical workload decomposition strategies for particle in cell (PIC) codes on Clusters of Symmetric MultiProcessors. The devised workload decomposition is hierarchically structured: a higher-level decomposition among the computational nodes, and a lower-level one among the processors of each computational node. Several decomposition strategies are evaluated by means of the prediction model, with respect to the memory occupancy, the parallelization efficiency and the required programming effort. Such strategies have been implemented by integrating the high-level languages High Performance Fortran (at the inter-node stage) and OpenMP (at the intra-node one). The details of these implementations are presented, and the experimental values of parallelization efficiency are compared with the predicted results.