SLES
solving a system of linear equations
Solves a tridiagonal linear system with SLES.
solving a system of linear equations
Solves a linear system in parallel with SLES.
Input parameters include:
-m <mesh_x> : number of mesh points in x-direction
-n <mesh_n> : number of mesh points in y-direction
basic parallel example;
Solves a linear system in parallel with SLES.
Input parameters include:
-random_exact_sol : use a random exact solution vector
-view_exact_sol : write exact solution vector to stdout
-m <mesh_x> : number of mesh points in x-direction
-n <mesh_n> : number of mesh points in y-direction
basic parallel example;
Solves a tridiagonal linear system.
Laplacian, 2d
Solves a linear system in parallel with SLES.
Input parameters include:
-m <mesh_x> : number of mesh points in x-direction
-n <mesh_n> : number of mesh points in y-direction
Laplacian, 2d
Solves a variable Poisson problem with SLES.
Laplacian, 2d
Solves a sequence of linear systems with different right-hand-side vectors.
Input parameters include:
-ntimes <ntimes> : number of linear systems to solve
-view_exact_sol : write exact solution vector to stdout
-m <mesh_x> : number of mesh points in x-direction
-n <mesh_n> : number of mesh points in y-direction
Laplacian, 2d
Solves a linear system in parallel with SLES.
Input parameters include:
-random_exact_sol : use a random exact solution vector
-view_exact_sol : write exact solution vector to stdout
-m <mesh_x> : number of mesh points in x-direction
-n <mesh_n> : number of mesh points in y-direction
basic parallel example
Solves a linear system in parallel with SLES. Also
illustrates setting a user-defined shell preconditioner and using the
macro __FUNCT__ to define routine names for use in error handling.
Input parameters include:
-user_defined_pc : Activate a user-defined preconditioner
basic parallel example
Solves a linear system in parallel with SLES. The matrix
uses simple bilinear elements on the unit square. To test the parallel
matrix assembly, the matrix is intentionally laid out across processors
differently from the way it is assembled. Input arguments are:
-m <size> : problem size
different matrices for linear system and preconditioner;
Uses a different preconditioner matrix and linear system matrix in the SLES solvers.
Note that different storage formats
can be used for the different matrices.
repeatedly solving linear systems;
Solves a sequence of linear systems with different right-hand-side vectors.
Input parameters include:
-ntimes <ntimes> : number of linear systems to solve
-view_exact_sol : write exact solution vector to stdout
-m <mesh_x> : number of mesh points in x-direction
-n <mesh_n> : number of mesh points in y-direction
repeatedly solving linear systems;
Solves two linear systems in parallel with SLES. The code
illustrates repeated solution of linear systems with the same preconditioner
method but different matrices (having the same nonzero structure). The code
also uses multiple profiling stages. Input arguments are
-m <size> : problem size
-mat_nonsym : use nonsymmetric matrix (default is symmetric)
repeatedly solving linear systems;
The solution of 2 different linear systems with different linear solvers.
Also, this example illustrates the repeated
solution of linear systems, while reusing matrix, vector, and solver data
structures throughout the process. Note the various stages of event logging.
customizing the block Jacobi preconditioner
Block Jacobi preconditioner for solving a linear system in parallel with SLES.
The code indicates the
procedures for setting the particular block sizes and for using different
linear solvers on the individual blocks.
Additive Schwarz Method (ASM) with user-defined subdomains
Illustrates use of the preconditioner ASM.
The Additive Schwarz Method for solving a linear system in parallel with SLES. The
code indicates the procedure for setting user-defined subdomains. Input
parameters include:
-user_set_subdomain_solvers: User explicitly sets subdomain solvers
-user_set_subdomains: Activate user-defined subdomains
solving a linear system
Reads a PETSc matrix and vector from a file and solves a linear system.
This version first preloads and solves a small system, then loads
another (larger) system and solves it as well. This example illustrates
preloading of instructions with the smaller system so that more accurate
performance monitoring can be done with the larger one (that actually
is the system of interest). See the 'Performance Hints' chapter of the
users manual for a discussion of preloading. Input parameters include
-f0 <input_file> : first file to load (small system)
-f1 <input_file> : second file to load (larger system)
-trans : solve transpose system instead
solving a Helmholtz equation
Solves a linear system in parallel with SLES.
basic sequential example
Solves a variable Poisson problem with SLES.