JXPAMG: a parallel algebraic multigrid solver for extreme-scale numerical simulations

JXPAMG is a parallel algebraic multigrid (AMG) solver for solving the extreme-scale, sparse linear systems on modern supercomputers. JXPAMG features the following characteristics: 1) It integrates some application-driven parallel AMG algorithms, including αSetup-AMG (adaptive Setup based AMG), AI-AMG (algebraic interface based AMG) and AMG-PCTL (physical-variable based coarsening two-level AMG); 2) A hierarchical parallel sparse matrix data structure, labeled hierarchical parallel Compressed Sparse Row (hpCSR), that matches the computer architecture is designed, and the highly scalable components based on hpCSR are implemented; 3) A flexible software architecture is designed to separate algorithm development from implementation. These characteristics allow JXPAMG to use different AMG strategies for different application features and architecture features, and thereby JXPAMG becomes aware of changes in these features. This paper introduces the algorithms, implementation techniques and applications of JXPAMG. Numerical experiments for typical real applications are given to illustrate the strong and weak parallel scaling properties of JXPAMG.