As a critical computation in numerical simulation applications such as large-scale scientific computing and industrial simulation, the solving rate of sparse linear equations directly determines the execution efficiency of computing tasks. However, sparse matrix computations are characterized by low computational intensity and high memory occupation, which results in performance bottlenecks in solving sparse linear equations. Many studies employed parallel optimization techniques to enhance the efficiency of solving sparse linear equations, but they all encountered many challenges such as low storage efficiency, load imbalance, and discontinuous memory access. Therefore, this paper first analyzes the challenges in improving the efficiency of solving sparse linear equations. Then the parallel optimization methods to improve the efficiency of solving ultra-large-scale sparse linear equations are sorted out from four key aspects, including optimization for the sparse matrix storage format, optimization for solving ultra-large-scale sparse linear equations, optimization for the basic operator of sparse matrix computation and mainstream sparse linear solver. Finally, this work concludes with a summary and a discussion of the directions that parallel optimization research in the future will go in solving ultra-large-scale sparse linear equations.

Managing software packages in a scientific computing environment is a challenging task, especially in the case of heterogeneous systems. It is error prone when installing and updating software packages in a sophisticated computing environment. Testing and performance evaluation in an on-the-fly manner is also a troublesome task for a production system. In this paper, we discuss a package management scheme based on containers. The newly developed method can ease the maintenance complexity and reduce human mistakes. We can benefit from the self-containing and isolation features of container technologies for maintaining the software packages among intricately connected clusters. By deploying the SuperComputing application Strore (SCStore) over the WAN connected world-largest clusters, it proved that it can greatly reduce the effort for maintaining the consistency of software environment and bring benefit to achieve automation.