Changes from Pasqua to example files and docs.

docs/src/gettingstarted.tex

@@ -219,14 +219,15 @@ Furthermore, specifying block-Jacobi as coarsest-level
 solver implies that the coarsest-level matrix is distributed
 among the processes.
 Figure~\ref{fig:ex3} shows how to set a W-cycle preconditioner which
-applies 2 Gauss-Seidel sweeps as pre- and post-smoother,
+applies 2 hybrid Gauss-Seidel sweeps as pre- and post-smoother,
 and solves the coarsest-level system with the multifrontal LU factorization
 implemented in MUMPS. It is specified that the coarsest-level
 matrix is distributed, since MUMPS can be used on both
 replicated and distributed matrices, and by default
-it is used on replicated ones. Note the use of the parameter \verb|pos|
+it is used on replicated ones. 
-to specify a property only for the pre-smoother or the post-smoother
+%Note the use of the parameter \verb|pos|
-(see Section~\ref{sec:precset} for more details).
+%to specify a property only for the pre-smoother or the post-smoother
+%(see Section~\ref{sec:precset} for more details).
 The code fragments shown in Figures~\ref{fig:ex2} and \ref{fig:ex3} are
 included in the example program file \verb|mld_dexample_ml.f90| too.
 
@@ -281,14 +282,13 @@ boundary conditions are also available in the directory \verb|examples/pdegen|.
 {\small
 \begin{verbatim}
 ... ...
-! build a W-cycle preconditioner with 2 Gauss-Seidel sweeps as 
+! build a W-cycle preconditioner with 2 hybrid Gauss-Seidel sweeps
-! pre- and post-smoother, a distributed coarsest
+! as pre- and post-smoother, a distributed coarsest
 ! matrix, and MUMPS as coarsest-level solver
   call P%init('ML',info)
-  call P%set('ML_TYPE','WCYCLE',info)
+  call P%set('ML_CYCLE','WCYCLE',info)
-  call P%set('SMOOTHER_TYPE','GS',info)
+  call P%set('SMOOTHER_TYPE','FBGS',info)
-  call P%set('SMOOTHER_SWEEPS',2,info,pos='PRE')
+  call P%set('SMOOTHER_SWEEPS',2,info)
-  call P%set('SMOOTHER_SWEEPS',2,info,pos='POST')
   call P%set('COARSE_SOLVE','MUMPS',info)
   call P%set('COARSE_MAT','DIST',info)
   call P%hierarchy_build(A,desc_A,info)

examples/fileread/mld_cexample_ml.f90

@@ -52,8 +52,8 @@
 ! (with ILU(0) on the blocks) as pre- and post-smoother, and 8 block-Jacobi
 ! sweeps (with ILU(0) on the blocks) as coarsest-level solver(Sec. 5.1, Fig. 3)
 !
-! - choice = 3, build a W-cycle preconditioner with 2 Gauss-Seidel sweeps as
+! - choice = 3, build a W-cycle preconditioner with 2 hybrid forward/backward
-! pre- and post-smoother, a distributed coarsest
+! GS sweeps as pre/post-smoother, a distributed coarsest
 ! matrix, and MUMPS as coarsest-level solver (Sec. 5.1, Fig. 4)
 !
 ! The matrix and the rhs are read from files (if an rhs is not available, the
@@ -231,15 +231,14 @@ program mld_cexample_ml
     
   case(3)
 
-   ! initialize a W-cycle preconditioner with 2 Gauss-Seidel sweeps as
+   ! initialize a W-cycle preconditioner with 2 hybrid forward/backward
-   ! post-smoother (and no pre-smoother), a distributed coarsest
+   ! GS sweeps as pre/post-smoother, a distributed coarsest
    ! matrix, and MUMPS as coarsest-level solver
 
     call P%init('ML',info)
-    call P%set('ML_TYPE','WCYCLE',info)
+    call P%set('ML_CYCLE','WCYCLE',info)
-    call P%set('SMOOTHER_TYPE','GS',info)
+    call P%set('SMOOTHER_TYPE','FBGS',info)
-    call P%set('SMOOTHER_SWEEPS',2,info,pos='PRE')
+    call P%set('SMOOTHER_SWEEPS',2,info)
-    call P%set('SMOOTHER_SWEEPS',2,info,pos='POST')
     call P%set('COARSE_SOLVE','MUMPS',info)
     call P%set('COARSE_MAT','DIST',info)
     kmethod = 'CG'

examples/fileread/mld_dexample_ml.f90

@@ -52,8 +52,8 @@
 ! (with ILU(0) on the blocks) as pre- and post-smoother, and 8 block-Jacobi
 ! sweeps (with ILU(0) on the blocks) as coarsest-level solver(Sec. 5.1, Fig. 3)
 !
-! - choice = 3, build a W-cycle preconditioner with 2 Gauss-Seidel sweeps as
+! - choice = 3, build a W-cycle preconditioner with 2 hybrid forward/backward
-! pre- and post-smoother, a distributed coarsest
+!  GS sweeps as pre/post-smoother, a distributed coarsest
 ! matrix, and MUMPS as coarsest-level solver (Sec. 5.1, Fig. 4)
 !
 ! The matrix and the rhs are read from files (if an rhs is not available, the
@@ -231,15 +231,14 @@ program mld_dexample_ml
     
   case(3)
 
-   ! initialize a W-cycle preconditioner with 2 Gauss-Seidel sweeps as
+   ! initialize a W-cycle preconditioner with 2 hybrid forward/backward
-   ! post-smoother (and no pre-smoother), a distributed coarsest
+   ! GS sweeps as pre/post-smoother, a distributed coarsest
    ! matrix, and MUMPS as coarsest-level solver
 
     call P%init('ML',info)
-    call P%set('ML_TYPE','WCYCLE',info)
+    call P%set('ML_CYCLE','WCYCLE',info)
-    call P%set('SMOOTHER_TYPE','GS',info)
+    call P%set('SMOOTHER_TYPE','FBGS',info)
-    call P%set('SMOOTHER_SWEEPS',2,info,pos='PRE')
+    call P%set('SMOOTHER_SWEEPS',2,info)
-    call P%set('SMOOTHER_SWEEPS',2,info,pos='POST')
     call P%set('COARSE_SOLVE','MUMPS',info)
     call P%set('COARSE_MAT','DIST',info)
     kmethod = 'CG'

examples/fileread/mld_sexample_ml.f90

@@ -52,8 +52,8 @@
 ! (with ILU(0) on the blocks) as pre- and post-smoother, and 8 block-Jacobi
 ! sweeps (with ILU(0) on the blocks) as coarsest-level solver(Sec. 5.1, Fig. 3)
 !
-! - choice = 3, build a W-cycle preconditioner with 2 Gauss-Seidel sweeps as
+! - choice = 3, build a W-cycle preconditioner with 2 hybrid forward/backward
-! pre- and post-smoother, a distributed coarsest
+! GS sweeps as pre/post-smoother, a distributed coarsest
 ! matrix, and MUMPS as coarsest-level solver (Sec. 5.1, Fig. 4)
 !
 ! The matrix and the rhs are read from files (if an rhs is not available, the
@@ -231,15 +231,14 @@ program mld_sexample_ml
     
   case(3)
 
-   ! initialize a W-cycle preconditioner with 2 Gauss-Seidel sweeps as
+   ! initialize a W-cycle preconditioner with 2 hybrid forward/backward
-   ! post-smoother (and no pre-smoother), a distributed coarsest
+   ! GS sweeps as pre/post-smoother, a distributed coarsest
    ! matrix, and MUMPS as coarsest-level solver
 
     call P%init('ML',info)
-    call P%set('ML_TYPE','WCYCLE',info)
+    call P%set('ML_CYCLE','WCYCLE',info)
-    call P%set('SMOOTHER_TYPE','GS',info)
+    call P%set('SMOOTHER_TYPE','FBGS',info)
-    call P%set('SMOOTHER_SWEEPS',2,info,pos='PRE')
+    call P%set('SMOOTHER_SWEEPS',2,info)
-    call P%set('SMOOTHER_SWEEPS',2,info,pos='POST')
     call P%set('COARSE_SOLVE','MUMPS',info)
     call P%set('COARSE_MAT','DIST',info)
     kmethod = 'CG'

examples/fileread/mld_zexample_ml.f90

@@ -52,8 +52,8 @@
 ! (with ILU(0) on the blocks) as pre- and post-smoother, and 8 block-Jacobi
 ! sweeps (with ILU(0) on the blocks) as coarsest-level solver(Sec. 5.1, Fig. 3)
 !
-! - choice = 3, build a W-cycle preconditioner with 2 Gauss-Seidel sweeps as
+! - choice = 3, build a W-cycle preconditioner with 2 hybrid forward/backward
-! pre- and post-smoother, a distributed coarsest
+! GS sweeps as pre/post-smoother, a distributed coarsest
 ! matrix, and MUMPS as coarsest-level solver (Sec. 5.1, Fig. 4)
 !
 ! The matrix and the rhs are read from files (if an rhs is not available, the
@@ -231,15 +231,14 @@ program mld_zexample_ml
     
   case(3)
 
-   ! initialize a W-cycle preconditioner with 2 Gauss-Seidel sweeps as
+   ! initialize a W-cycle preconditioner with 2 hybrid forward/backward
-   ! post-smoother (and no pre-smoother), a distributed coarsest
+   ! GS sweeps as pre/post-smoother, a distributed coarsest
    ! matrix, and MUMPS as coarsest-level solver
 
     call P%init('ML',info)
-    call P%set('ML_TYPE','WCYCLE',info)
+    call P%set('ML_CYCLE','WCYCLE',info)
-    call P%set('SMOOTHER_TYPE','GS',info)
+    call P%set('SMOOTHER_TYPE','FBGS',info)
-    call P%set('SMOOTHER_SWEEPS',2,info,pos='PRE')
+    call P%set('SMOOTHER_SWEEPS',2,info)
-    call P%set('SMOOTHER_SWEEPS',2,info,pos='POST')
     call P%set('COARSE_SOLVE','MUMPS',info)
     call P%set('COARSE_MAT','DIST',info)
     kmethod = 'CG'

examples/fileread/runs/ml.inp

@@ -1,6 +1,6 @@
 mld_mat.mtx              ! This matrix (and others) from: http://math.nist.gov/MatrixMarket/ or 
 mld_rhs.mtx              ! rhs          | http://www.cise.ufl.edu/research/sparse/matrices/index.html
 MM                       ! File format: MM (Matrix Market)  HB (Harwell-Boeing).
-1                        ! Preconditioner choice 
+3                        ! Preconditioner choice 
 0100                     ! ITMAX
 1.d-6                    ! EPS

examples/pdegen/mld_dexample_ml.f90

@@ -53,8 +53,8 @@
 ! (with ILU(0) on the blocks) as pre- and post-smoother, and 8 block-Jacobi
 ! sweeps (with ILU(0) on the blocks) as coarsest-level solver(Sec. 5.1, Fig. 3)
 !
-! - choice = 3, build a W-cycle preconditioner with 2 Gauss-Seidel sweeps as
+! - choice = 3, build a W-cycle preconditioner with 2 hybrid forward/backward
-! pre- and post-smoother, a distributed coarsest
+! GS sweeps as pre/post-smoother, a distributed coarsest
 ! matrix, and MUMPS as coarsest-level solver (Sec. 5.1, Fig. 4)
 !
 ! The PDE is a general second order equation in 3d
@@ -248,15 +248,14 @@ program mld_dexample_ml
 
   case(3)
 
-   ! initialize a W-cycle preconditioner with 2 Gauss-Seidel sweeps as
+   ! initialize a W-cycle preconditioner with 2 hybrid forward/backward
-   ! post-smoother (and no pre-smoother), a distributed coarsest
+   ! GS sweeps as pre/post-smoother, a distributed coarsest
    ! matrix, and MUMPS as coarsest-level solver
 
     call P%init('ML',info)
-    call P%set('ML_TYPE','WCYCLE',info)
+    call P%set('ML_CYCLE','WCYCLE',info)
-    call P%set('SMOOTHER_TYPE','GS',info)
+    call P%set('SMOOTHER_TYPE','FBGS',info)
-    call P%set('SMOOTHER_SWEEPS',2,info,pos='PRE')
+    call P%set('SMOOTHER_SWEEPS',2,info)
-    call P%set('SMOOTHER_SWEEPS',2,info,pos='POST')
     call P%set('COARSE_SOLVE','MUMPS',info)
     call P%set('COARSE_MAT','DIST',info)
     kmethod = 'CG'

examples/pdegen/mld_sexample_ml.f90

@@ -53,8 +53,8 @@
 ! (with ILU(0) on the blocks) as pre- and post-smoother, and 8 block-Jacobi
 ! sweeps (with ILU(0) on the blocks) as coarsest-level solver(Sec. 5.1, Fig. 3)
 !
-! - choice = 3, build a W-cycle preconditioner with 2 Gauss-Seidel sweeps as
+! - choice = 3, build a W-cycle preconditioner with 2 hybrid forward/backward
-! pre- and post-smoother, a distributed coarsest
+! GS sweeps pre/post-smoother, a distributed coarsest
 ! matrix, and MUMPS as coarsest-level solver (Sec. 5.1, Fig. 4)
 !
 ! The PDE is a general second order equation in 3d
@@ -248,15 +248,14 @@ program mld_sexample_ml
 
   case(3)
 
-   ! initialize a W-cycle preconditioner with 2 Gauss-Seidel sweeps as
+   ! initialize a W-cycle preconditioner with 2 hybrid forward/backward
-   ! post-smoother (and no pre-smoother), a distributed coarsest
+   ! GS sweeps as pre/post-smoother, a distributed coarsest
    ! matrix, and MUMPS as coarsest-level solver
 
     call P%init('ML',info)
-    call P%set('ML_TYPE','WCYCLE',info)
+    call P%set('ML_CYCLE','WCYCLE',info)
-    call P%set('SMOOTHER_TYPE','GS',info)
+    call P%set('SMOOTHER_TYPE','FBGS',info)
-    call P%set('SMOOTHER_SWEEPS',2,info,pos='PRE')
+    call P%set('SMOOTHER_SWEEPS',2,info)
-    call P%set('SMOOTHER_SWEEPS',2,info,pos='POST')
     call P%set('COARSE_SOLVE','MUMPS',info)
     call P%set('COARSE_MAT','DIST',info)
     kmethod = 'CG'

examples/pdegen/runs/1lev.inp

@@ -1,3 +1,3 @@
 15            ! IDIM; domain size is idim**3
-10          ! ITMAX
+100          ! ITMAX
 1.d-6         ! EPS

examples/pdegen/runs/ml.inp

@@ -1,4 +1,4 @@
-2            ! Preconditioner choice 
+3            ! Preconditioner choice 
 10            ! IDIM; domain size is idim**3
 500          ! ITMAX
 1.d-6         ! EPS