As a result of the interplay between advances in computer hardware, software, and algorithm, we are now in a new era of large-scale reservoir simulation, which focuses on accurate flow description, fine reservoir char...As a result of the interplay between advances in computer hardware, software, and algorithm, we are now in a new era of large-scale reservoir simulation, which focuses on accurate flow description, fine reservoir characterization, efficient nonlinear/linear solvers, and parallel implementation. In this paper, we discuss a multilevel preconditioner in a new-generation simulator and its implementation on multicore computers. This preconditioner relies on the method of subspace corrections to solve large-scale linear systems arising from fully implicit methods in reservoir simulations. We investigate the parallel efficiency and robustness of the proposed method by applying it to million-cell benchmark problems.展开更多
In shared-memory bus-based multiprocessors, when the number of processors grows, the processors spend an increasing amount of time waiting for access to the bus (and shared memory). This contention reduces the perform...In shared-memory bus-based multiprocessors, when the number of processors grows, the processors spend an increasing amount of time waiting for access to the bus (and shared memory). This contention reduces the performance of processors and imposes a limitation of the number of processors that can be used efficiently in bus-based systems. Since the multi-processor’s performance depends upon many parameters which affect the performance in different ways, timed Petri nets are used to model shared-memory bus-based multiprocessors at the instruction execution level, and the developed models are used to study how the performance of processors changes with the number of processors in the system. The results illustrate very well the restriction on the number of processors imposed by the shared bus. All performance characteristics presented in this paper are obtained by discrete-event simulation of Petri net models.展开更多
为了提高作物生长模型的计算速度,论文提出了多种分布式并行调度方案。综合分析了田块尺度下作物生长子模型以及子模型内部组分的数据依赖关系和计算流程。以流水线技术和分治策略为基础,分别在模型组分层、子模型层和驱动数据层设计了...为了提高作物生长模型的计算速度,论文提出了多种分布式并行调度方案。综合分析了田块尺度下作物生长子模型以及子模型内部组分的数据依赖关系和计算流程。以流水线技术和分治策略为基础,分别在模型组分层、子模型层和驱动数据层设计了不同的分布式并行调度方案。在WCCS2003(Windows Compute Cluster Server 2003)组成的PC集群环境下,分别采用了OpenMP、MPI_OpenMP混合以及MPI编程模型实现了多种调度方案的并行模拟。并行加速比的实验分析表明,优化后的子模型层并行调度方案,在6个双核CPUs组成的PC集群上的平均加速比可达到8.2,更接近模型并行计算加速比的预测值。在分布式集群环境下,采用基于MPI的子模型层中等粒度的并行调度方案具有更快的计算速度,更适合于作物生长模拟系统。展开更多
基金support through PetroChina New-generation Reservoir Simulation Software (2011A-1010)the Program of Research on Continental Sedimentary Oil Reservoir Simulation (z121100004912001)+7 种基金founded by Beijing Municipal Science & Technology Commission and PetroChina Joint Research Funding12HT1050002654partially supported by the NSFC Grant 11201398Hunan Provincial Natural Science Foundation of China Grant 14JJ2063Specialized Research Fund for the Doctoral Program of Higher Education of China Grant 20124301110003partially supported by the Dean’s Startup Fund, Academy of Mathematics and System Sciences and the State High Tech Development Plan of China (863 Program 2012AA01A309partially supported by NSFC Grant 91130002Program for Changjiang Scholars and Innovative Research Team in University of China Grant IRT1179the Scientific Research Fund of the Hunan Provincial Education Department of China Grant 12A138
文摘As a result of the interplay between advances in computer hardware, software, and algorithm, we are now in a new era of large-scale reservoir simulation, which focuses on accurate flow description, fine reservoir characterization, efficient nonlinear/linear solvers, and parallel implementation. In this paper, we discuss a multilevel preconditioner in a new-generation simulator and its implementation on multicore computers. This preconditioner relies on the method of subspace corrections to solve large-scale linear systems arising from fully implicit methods in reservoir simulations. We investigate the parallel efficiency and robustness of the proposed method by applying it to million-cell benchmark problems.
文摘In shared-memory bus-based multiprocessors, when the number of processors grows, the processors spend an increasing amount of time waiting for access to the bus (and shared memory). This contention reduces the performance of processors and imposes a limitation of the number of processors that can be used efficiently in bus-based systems. Since the multi-processor’s performance depends upon many parameters which affect the performance in different ways, timed Petri nets are used to model shared-memory bus-based multiprocessors at the instruction execution level, and the developed models are used to study how the performance of processors changes with the number of processors in the system. The results illustrate very well the restriction on the number of processors imposed by the shared bus. All performance characteristics presented in this paper are obtained by discrete-event simulation of Petri net models.
文摘为了提高作物生长模型的计算速度,论文提出了多种分布式并行调度方案。综合分析了田块尺度下作物生长子模型以及子模型内部组分的数据依赖关系和计算流程。以流水线技术和分治策略为基础,分别在模型组分层、子模型层和驱动数据层设计了不同的分布式并行调度方案。在WCCS2003(Windows Compute Cluster Server 2003)组成的PC集群环境下,分别采用了OpenMP、MPI_OpenMP混合以及MPI编程模型实现了多种调度方案的并行模拟。并行加速比的实验分析表明,优化后的子模型层并行调度方案,在6个双核CPUs组成的PC集群上的平均加速比可达到8.2,更接近模型并行计算加速比的预测值。在分布式集群环境下,采用基于MPI的子模型层中等粒度的并行调度方案具有更快的计算速度,更适合于作物生长模拟系统。
