2017/01/20 by Hui Liu, Zhangxin Chen, Liu, Hui +1 · 2 citations
Computer Science · Engineering · Mathematics · #Advanced Numerical Methods in Computational Mathematics #Algorithm #Biconjugate gradient method #Biconjugate gradient stabilized method #Computation #Computational science #Computer science #Conjugate residual method #Distributed memory #Generalized minimal residual method #Interface (matter) #Iterative method #Krylov subspace #Linear algebra #Linear system #Mathematics #Matrix Theory and Algorithms #Message Passing Interface #Message passing #Multigrid method #Numerical linear algebra #Parallel Computing and Optimization Techniques #Parallel computing #Scalability #Shared memory #Solver #Sparse matrix #cs.MS
paper · pdf · doi:10.48550/arxiv.1701.05913
published in arXiv (Cornell University) (Cornell University) · arXiv admin note: substantial text overlap with arXiv:1602.05901
arxiv created 2017/01/20 · openalex publication_date 2017/01/20 · arxiv updated 2017/01/24 · openalex created_date 2017/02/03 · openalex updated_date 2026/07/28
This paper presents our work on designing scalable linear solvers for large-scale reservoir simulations. The main objective is to support implementation of parallel reservoir simulators on distributed-memory parallel systems, where MPI (Message Passing Interface) is employed for communications among computation nodes. Distributed matrix and vector modules are designed, which are the base of our parallel linear systems. Commonly-used Krylov subspace linear solvers are implemented, including the restarted GMRES method, the LGMRES method, and the BiCGSTAB method. It also has an interface to a parallel algebraic multigrid solver, BoomerAMG from HYPRE. Parallel general-purpose preconditioners and special preconditioners for reservoir simulations are also developed. The numerical experiments show that our linear solvers have excellent scalability using thousands of CPU cores.