Survey of Linear Algebra Solvers for Exascale Computing.

The application of scientific engineering computing based on exascale computing not only offers opportunities but also creates challenges for the development of numerical linear algebra algorithms. Firstly, the characteristics of exascale computing are analyzed, including: parallel programming for large-scale heterogeneous parallel architecture has become the mainstream approach; reducing the extremely high energy costs associated with running large- scale applications is a major concern; multi-precision heterogeneous computing hardware has triggered further research of mixed precision computing. Secondly, the optimization work of mainstream dense and sparse linear algebra solvers for high-performance computing architectures is reviewed, and the characteristics and advantages of each solver are compared. Then, the main technology progress of linear algebra solvers is summarized, mainly including: isolating heterogeneous computing modules and designing a new unified programming framework to achieve performance portability of software algorithms; improving the performance level of numerical computing and data storage using mixed precision methods while ensuring the overall requirements of scientific engineering computing applications; combined with hardware multi-level cache and network communication characteristics, advanced parallel computing algorithms are developed to avoid or reduce inefficient large- scale data communication. Finally, this paper provides an outlook on the future research trends in this direction. [ABSTRACT FROM AUTHOR]