In this research,we present the pure open multi-processing(OpenMP),pure message passing interface(MPI),and hybrid MPI/OpenMP parallel solvers within the dynamic explicit central difference algorithm for the coining pr...In this research,we present the pure open multi-processing(OpenMP),pure message passing interface(MPI),and hybrid MPI/OpenMP parallel solvers within the dynamic explicit central difference algorithm for the coining process to address the challenge of capturing fine relief features of approximately 50 microns.Achieving such precision demands the utilization of at least 7 million tetrahedron elements,surpassing the capabilities of traditional serial programs previously developed.To mitigate data races when calculating internal forces,intermediate arrays are introduced within the OpenMP directive.This helps ensure proper synchronization and avoid conflicts during parallel execution.Additionally,in the MPI implementation,the coins are partitioned into the desired number of regions.This division allows for efficient distribution of computational tasks across multiple processes.Numerical simulation examples are conducted to compare the three solvers with serial programs,evaluating correctness,acceleration ratio,and parallel efficiency.The results reveal a relative error of approximately 0.3%in forming force among the parallel and serial solvers,while the predicted insufficient material zones align with experimental observations.Additionally,speedup ratio and parallel efficiency are assessed for the coining process simulation.The pureMPI parallel solver achieves a maximum acceleration of 9.5 on a single computer(utilizing 12 cores)and the hybrid solver exhibits a speedup ratio of 136 in a cluster(using 6 compute nodes and 12 cores per compute node),showing the strong scalability of the hybrid MPI/OpenMP programming model.This approach effectively meets the simulation requirements for commemorative coins with intricate relief patterns.展开更多
阐述MPI与OpenMP进行并行计算的特点,并在Visual Studio 2010上构建一个基于两者的混合编程平台。程序在该平台上执行时能够同时实现多进程与进程内多线程编程,设计并实现一种基于数据划分的矩阵乘法的并行算法,将数据分解为两部分交给...阐述MPI与OpenMP进行并行计算的特点,并在Visual Studio 2010上构建一个基于两者的混合编程平台。程序在该平台上执行时能够同时实现多进程与进程内多线程编程,设计并实现一种基于数据划分的矩阵乘法的并行算法,将数据分解为两部分交给两个计算节点分别完成,并在每个计算节点内将数据进一步划分,交给多个线程同时执行。通过与非并行矩阵乘法、MPI矩阵乘法、OpenMP矩阵乘法运算性能进行比较,验证该算法可以有效地挖掘计算机的处理能力。展开更多
基金supported by the fund from ShenyangMint Company Limited(No.20220056)Senior Talent Foundation of Jiangsu University(No.19JDG022)Taizhou City Double Innovation and Entrepreneurship Talent Program(No.Taizhou Human Resources Office[2022]No.22).
文摘In this research,we present the pure open multi-processing(OpenMP),pure message passing interface(MPI),and hybrid MPI/OpenMP parallel solvers within the dynamic explicit central difference algorithm for the coining process to address the challenge of capturing fine relief features of approximately 50 microns.Achieving such precision demands the utilization of at least 7 million tetrahedron elements,surpassing the capabilities of traditional serial programs previously developed.To mitigate data races when calculating internal forces,intermediate arrays are introduced within the OpenMP directive.This helps ensure proper synchronization and avoid conflicts during parallel execution.Additionally,in the MPI implementation,the coins are partitioned into the desired number of regions.This division allows for efficient distribution of computational tasks across multiple processes.Numerical simulation examples are conducted to compare the three solvers with serial programs,evaluating correctness,acceleration ratio,and parallel efficiency.The results reveal a relative error of approximately 0.3%in forming force among the parallel and serial solvers,while the predicted insufficient material zones align with experimental observations.Additionally,speedup ratio and parallel efficiency are assessed for the coining process simulation.The pureMPI parallel solver achieves a maximum acceleration of 9.5 on a single computer(utilizing 12 cores)and the hybrid solver exhibits a speedup ratio of 136 in a cluster(using 6 compute nodes and 12 cores per compute node),showing the strong scalability of the hybrid MPI/OpenMP programming model.This approach effectively meets the simulation requirements for commemorative coins with intricate relief patterns.
文摘阐述MPI与OpenMP进行并行计算的特点,并在Visual Studio 2010上构建一个基于两者的混合编程平台。程序在该平台上执行时能够同时实现多进程与进程内多线程编程,设计并实现一种基于数据划分的矩阵乘法的并行算法,将数据分解为两部分交给两个计算节点分别完成,并在每个计算节点内将数据进一步划分,交给多个线程同时执行。通过与非并行矩阵乘法、MPI矩阵乘法、OpenMP矩阵乘法运算性能进行比较,验证该算法可以有效地挖掘计算机的处理能力。
