Since some computation in reachability Query Preserving Graph Compression （QPGC） algorithm are redundant， a high-performance compression strategy was proposed. In the stage of solving the vertex sets of ancestors and descendants， an algorithm named TSB （Topological Sorting Based algorithm for solving ancestor and descendant sets） was proposed for common graph data. Firstly， the vertices of the graph data were topological sorted. Then， the vertex sets were solved in the order or backward order of the topological sequence， avoiding the redundant computation caused by the ambiguous solution order. And an algorithm based on graph aggregation operation was proposed for graph data with short longest path， namely AGGB （AGGregation Based algorithm for solving ancestor and descendant sets）， so the vertex sets were able to be solved in a certain number of aggregation operations. In the stage of solving reachability equivalence class， a Piecewise Statistical Pruning （PSP） algorithm was proposed. Firstly， piecewise statistics of ancestors and descendants sets were obtained and then the statistics were compared to achieve the coarse matching， and some unnecessary fine matches were pruned off. Experimental results show that compared with QPGC algorithm： in the stage of solving the vertex sets of ancestors and descendants， TSB and AGGB algorithm have the performance averagely increased by 94.22% and 90.00% respectively on different datasets； and in the stage of solving the reachability equivalence class， PSP algorithm has the performance increased by more than 70% on most datasets. With the increasing of the dataset， using TSB and AGGB cooperated with PSP has the performance improved by nearly 28 times. Theoretical analysis and simulation results show that the proposed strategy has less redundant computation and faster compression speed compared to QPGC.

When using Immersed Boundary-Lattice Boltzmann Method （IB-LBM） to solve the flow field， in order to obtain more accurate results， a larger and denser flow field grid is often required， which results in a long time of simulation process. In order to improve the efficiency of the simulation， according to the characteristics of IB-LBM local calculation， combined with three different task scheduling methods in OpenMP， a parallel optimization method of IB-LBM was proposed. In the parallel optimization， three task scheduling modes were mixed to solve the load imbalance problem caused by single task scheduling. The structural decomposition was performed on IB-LBM， and the optimal scheduling mode of each structure part was tested. Based on the experimental results， the optimal scheduling combination mode was selected. At the same time， it could be concluded that the optimal combination is different under different thread counts. The optimization results were verified by speedup， and it could be concluded that when the number of threads is small， the speedup approaches the ideal state； when the number of threads is large， although the additional time consumption of developing and destroying threads affects the optimization of performance， the parallel performance of the model is still greatly improved. The flow field simulation results show that the accuracy of IB-LBM simulation of fluid-solid coupling problems is not affected after parallel optimization.