Parallel multigrid is widely used as preconditioners in solving large-scale sparse linear systems. However, the current multigrid library still needs more satisfactory performance for structured grid problems regarding speed and scalability. To this end, we design and implement StructMG, a fast and scalable multigrid that constructs hierarchical grids automatically based on the original matrix. As a preconditioner, StructMG can achieve both low cost per iteration and good convergence. Two idealized and five real-world problems from four application fields, including radiation hydrodynamics, petroleum reservoir simulation, numerical weather prediction, and solid mechanics, are evaluated on ARM and X86 platforms. In comparison to hypre's multigrid preconditioners, StructMG achieves the fastest time-to-solutions in all cases with average speedups of 17.6x, 5.7x, 4.6x, 8.5x over SMG, PFMG, SysPFMG, and BoomerAMG, respectively. Additionally, StructMG significantly improves strong and weak scaling efficiencies in most tests.

Abstract It is becoming increasingly significant for humans to predict and understand future climate changes using coupled climate system models. Although the performance and scalability of individual physical components have improved over the past few years, coupled climate systems still suffer from low efficiency. This paper focuses on the process scheduling problem for the widely applied coupled earth system model (CESM). The proposed resource allocation strategies allow components to execute on a compromised suboptimal setup and still maintain approximately the best parallel speedup. With this flexible resource allocation strategy, we further propose a coordinated process scheduling algorithm (CPSA). More notably, we propose an upgraded version called CPSA‐B, which makes efficient resource sharing configurations, including resource allocation and process layout of components. We integrate CPSA and CPSA‐B as pre‐arrangement tools into the CESM program and deploy them on the Huawei Kunpeng platform. The speedup curves of the CESM components are prepared in advance, based on sampling tests. Experimental data show that CPSA‐B reduces up to 58% of the execution time compared with the CESM default strategy. The algorithm has low complexity and can efficiently find solutions for large input sizes.

Coupled climate system models are important tools for climatologists to predict and understand future climate. These models are usually resource-consuming due to the large number of processors required and long execution time. Although the performance and scalability of individual physical system model have been improved over the past years, coupled climate systems still suffer from low efficiency when sharing resource across models. This paper focuses on the process scheduling strategy of Coupled Earth System Model (CESM), a widely applied coupled system model. Instead of pursuing best speedup efficiency for individual component, the proposed resource allocation strategy allows components to execute on compromised sub-optimal setup and still maintains relatively high parallel speedup. With this flexible resource allocation strategy, we further propose a Coordinated Process Scheduling Algorithm (CPSA) to make efficient resource sharing configurations, including resource allocation and process layout of components. We integrate CPSA as a tool into CESM program, and deploy it on Huawei Kunpeng Platform. Speedup curves of CESM components are prepared in advance based on sampling tests. Experimental data show that our algorithm reduces up to 52.6% of execution time compared with CESM default strategy. We also present simulation data to show that our algorithm is efficient for the platforms with up to a million cores.

Grid computing technology is able to integrate and share large-scale distributed computation and data resource to facilitate the scientific researches. Recently, the grid workflow support and large-scale distributed data management are becoming two main requirements of scientists and researchers in many fields, such as bioinformatics, high-energy physics etc. In this paper, we proposed to support grid workflow for data intensive applications using CSF4 scheduling plug-ins. The grid workflow scheduling and data aware scheduling policies are implemented in two scheduling plug-ins, grid workflow plug-in and grid data aware plug-in, respectively. The two scheduling plug-ins can work together smoothly. The data aware plug-in will automatically dispatch the workflow tasks to the grid sites which are close to data replicas. At last, the experiment results are given to show the improvement of system performance and optimization of scheduling.