In this paper, the repeatable hybrid parallel implementation of inverse matrix computation using SMW formula is proposed. The authors' had previously proposed a hybrid parallel algorithm for inverse matrix computation. It is reasonably fast for a one time computation of an inverse matrix, but it is hard to apply this algorithm repeatedly for consecutive computations since the relocation of the large matrix is required at the beginning of each iterations. In order to eliminate the relocation of the large input matrix which is the output of the inverse matrix computation from the previous time step, the computation algorithm has been redesigned so that the required portion of the input matrix becomes the same as the output portion of the previously computed matrix in each node. This makes it possible to repeatedly and efficiently apply the SMW formula to compute inverse matrix in a time-series simulation.

In this paper, a hybrid parallel implementation of inverse matrix computation using SMW formula is proposed. By aggregating the memory bandwidth in the hybrid parallel implementation, the bottleneck due to the memory bandwidth limitation in the authors previous multicore implementation has been dissolved. More than 8 times of speed up is also achieved with dual-core 8-nodes implementation which leads more than 20 simulation steps per second, or near real-time performance.

Simulation based education and training, especially wargame simulations, are being used widely in the field of defense modeling and in simulation communities. In order to efficiently train students and trainees, the wargame simulations must have both high performance and high fidelity. In this paper, we discuss design and implementation issues for a prototype of a parallel and distributed wargame simulation system. This wargame simulation system is based on High Level Architecture (HLA) and employs some optimization to achieve both high performance and high fidelity in the simulation system. The results show that the proposed optimization method is effective when optimization is applied to 93.5% or less of the moving objects (PFs) within the range of detection (RofD) of both the red and blue teams. Specifically, when each team has 1000 PFs we found that if the percentage of PFs within RofD is less than 50% for both teams, our method is over two times better than for the situation where there is no optimization.