Package: wnpp Severity: wishlist Owner: Drew Parsons <dpars...@debian.org>
* Package name : superlu-dist Version : 5.1.3 Upstream Author : Xiaoye S. Li <x...@lbl.gov> * URL : http://crd-legacy.lbl.gov/~xiaoye/SuperLU/#superlu_dist * License : BSD Programming Lang: C Description : Highly distributed solution of large, sparse systems of linear equations SuperLU is a general purpose library for the direct solution of large, sparse, nonsymmetric systems of linear equations. The library is written in C and is callable from either C or Fortran program. It uses MPI, OpenMP and CUDA to support various forms of parallelism. It supports both real and complex datatypes, both single and double precision, and 64-bit integer indexing. The library routines performs an LU decomposition with partial pivoting and triangular system solves through forward and back substitution. The LU factorization routines can handle non-square matrices but the triangular solves are performed only for square matrices. The matrix columns may be preordered (before factorization) either through library or user supplied routines. This preordering for sparsity is completely separate from the factorization. Working precision iterative refinement subroutines are provided for improved backward stability. Routines are also provided to equilibrate the system, estimate the condition number, calculate the relative backward error, and estimate error bounds for the refined solutions. SuperLU_DIST implements the algorithms for distributed memory, targetting highly parallel distributed memory hybrid systems. The numerical factorization routines are already implemented for hybrid systems with multiple GPUs. Further work will be needed to implement the other phases of the algorithms on the hybrid systems and to enhance strong scaling to extreme scale. This package will be maintained under the Debian Science team. superlu-dist complements the existing superlu package, which is the sequential implementation of SuperLU. SuperLU_DIST is used by PETSc and FEniCS for numerical computation.