Lis (linear algebra library)

Lis (Library of Iterative Solvers for linear systems, pronounced [lis]) is a scalable parallel software library for solving discretized linear equations and eigenvalue problems that mainly arise in the numerical solution of partial differential equations by using iterative methods.[1][2][3] Although it is designed for parallel computers, the library can be used without being conscious of parallel processing.

Lis
Stable release
2.0.22 / May 22, 2020 (2020-05-22)
Operating systemCross-platform
Available inC, Fortran
TypeSoftware library
LicenseNew BSD License
Websitewww.ssisc.org/lis/

Features

Lis provides facilities for:

Example

A C program to solve the linear equation is written as follows:

#include <stdio.h>
#include "lis_config.h"
#include "lis.h"

LIS_INT main(LIS_INT argc, char* argv[])
{
  LIS_MATRIX  A;
  LIS_VECTOR  b, x;
  LIS_SOLVER  solver;
  LIS_INT     iter;
  double      time;

  lis_initialize(&argc, &argv);

  lis_matrix_create(LIS_COMM_WORLD, &A);
  lis_vector_create(LIS_COMM_WORLD, &b);
  lis_vector_create(LIS_COMM_WORLD, &x);

  lis_input_matrix(A, argv[1]);
  lis_input_vector(b, argv[2]);
  lis_vector_duplicate(A, &x);

  lis_solver_create(&solver);
  lis_solver_set_optionC(solver);
  lis_solve(A, b, x, solver);

  lis_solver_get_iter(solver, &iter);
  lis_solver_get_time(solver, &time);
  printf("number of iterations = %d\n", iter);
  printf("elapsed time = %e\n", time);

  lis_output_vector(x, LIS_FMT_MM, argv[3]);

  lis_solver_destroy(solver);
  lis_matrix_destroy(A);
  lis_vector_destroy(b);
  lis_vector_destroy(x);

  lis_finalize();

  return 0;
}

System requirements

The installation of Lis requires a C compiler. The Fortran interface requires a Fortran compiler, and the algebraic multigrid preconditioner requires a Fortran 90 compiler.[4] For parallel computing environments, an OpenMP or MPI library is required. Both the Matrix Market and Harwell-Boeing formats are supported to import and export user data.

Packages that use Lis
gollark: I disagree entirely.
gollark: We have the worst of both worlds in many places, with nigh-identical rows of houses which are apparently still built slowly and inefficiently.
gollark: If it was made of multiple cuboids, you could even put them together in exciting ways.
gollark: It isn't like you couldn't paint a cuboid to look nice.
gollark: Do you actually spend enough time admiring your house that the substantially greater cost would be any use?

See also

References
  1. Akira Nishida (2010). "Experience in Developing an Open Source Scalable Software Infrastructure in Japan". Computational Science and Its Applications – ICCSA 2010. Lecture Notes in Computer Science 6017. 6017. Springer. pp. 87–98. doi:10.1007/978-3-642-12165-4_36. ISBN 978-3-642-12164-7.
  2. Hisashi Kotakemori; Hidehiko Hasegawa; Tamito Kajiyama; Akira Nukada; Reiji Suda & Akira Nishida (2008). "Performance Evaluation of Parallel Sparse Matrix-Vector Products on SGI Altix 3700". OpenMP Shared Memory Parallel Programming. Lecture Notes in Computer Science 4315. Springer. pp. 153–163. doi:10.1007/978-3-540-68555-5_13. ISBN 978-3-540-68554-8.
  3. Hisashi Kotakemori; Hidehiko Hasegawa & Akira Nishida (2005). "Performance Evaluation of a Parallel Iterative Method Library using OpenMP". Proceedings of the 8th International Conference on High Performance Computing in Asia Pacific Region (HPC Asia 2005). IEEE. pp. 432–436. doi:10.1109/HPCASIA.2005.74. ISBN 0-7695-2486-9.
  4. Akihiro Fujii; Akira Nishida & Yoshio Oyanagi (2005). "Evaluation of Parallel Aggregate Creation Orders : Smoothed Aggregation Algebraic Multigrid Method". High Performance Computational Science and Engineering. Springer. pp. 99–122. doi:10.1007/0-387-24049-7_6. ISBN 1-4419-3684-X.
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