Name
Abstract | ||
|---|---|---|
The hierarchical interpolative factorization (HIF) offers an efficient way for solving or preconditioning elliptic partial differential equations. By exploiting locality and low-rank properties of the operators, the HIF achieves quasi-linear complexity for factorizing the discrete positive definite elliptic operator and linear complexity for solving the associated linear system. In this paper, the distributed-memory HIF (DHIF) is introduced as a parallel and distributed-memory implementation of the HIF. The DHIF organizes the processes in a hierarchical structure and keeps the communication as local as possible. The computation complexity is \(O(\frac{N\log N}{P})\) and \(O(\frac{N}{P})\) for constructing and applying the DHIF, respectively, where N is the size of the problem and P is the number of processes. The communication complexity is \(O(\sqrt{P}\log ^3 P)\alpha + O(\frac{N^{2/3}}{\sqrt{P}})\beta \) where \(\alpha \) is the latency and \(\beta \) is the inverse bandwidth. Extensive numerical examples are performed on the NERSC Edison system with up to 8192 processes. The numerical results agree with the complexity analysis and demonstrate the efficiency and scalability of the DHIF. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1186/s40687-017-0100-6 | Research in the Mathematical Sciences |
Keywords | Field | DocType |
Sparse matrix, Multifrontal, Elliptic problem, Matrix factorization, Structured matrix, 44A55, 65R10, 65T50 | Inverse,Combinatorics,Linear system,Elliptic operator,Positive-definite matrix,Matrix decomposition,Communication complexity,Factorization,Elliptic partial differential equation,Mathematics | Journal |
Volume | Issue | ISSN |
abs/1607.00346 | 1 | 2522-0144 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yingzhou Li | 1 | 0 | 0.68 |
| Lexing Ying | 2 | 1273 | 103.92 |