"研究云计算下的偏微分方程并行计算问题：架构、程序设计和实验环境"

nvestigate the parallel computing strategies for solving partial differential equations. We discuss the finite difference method, finite element method, and finite volume method for discretizing partial differential equations. We also introduce the concept of parallel computing and how it can be used to solve these equations efficiently. In addition, we explore the use of MPI (Message Passing Interface) programming for parallel computing and provide a detailed explanation of how MPI can be implemented to solve partial differential equations. In the part 3, including chapter 4 and chapter 5, we present several numerical experiments to validate the effectiveness of the parallel computing strategies discussed in the previous sections. We compare the performance of different parallel algorithms for solving partial differential equations and analyze the scalability and efficiency of these algorithms. Through these experiments, we demonstrate the advantages of using parallel computing for solving partial differential equations and show how it can significantly improve the computational efficiency and accuracy of the solutions. Overall, this paper provides a comprehensive study of parallel computing strategies for solving partial differential equations. By investigating various parallel algorithms and conducting numerical experiments, we show the potential of parallel computing in improving the efficiency and accuracy of solving partial differential equations. This research contributes to the development of computational methods for solving complex differential equations and demonstrates the importance of parallel computing in scientific computing.