html/img1.png
 html/img32.png
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 html/img58.png
 html/img71.png
 html/img83.png
 html/node11.html
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 html/node3.html
 pdf
 pdf/Makefile
 pdf/abstract.tex
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 src
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 src/background.tex
 src/bibliography.tex
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 src/figures
 src/gettingstarted.tex
 src/intro.tex
 src/license.tex
 src/overview.tex
 src/precs.tex
 src/title.tex
 src/tmp
 src/userguide.tex
 src/userhtml.tex
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Docs: created SRC subdir.
stopcriterion
Salvatore Filippone 16 years ago
parent 1b4d970e6f
commit ef5a0f3d99

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@ -276,7 +276,7 @@ three steps:
ALT="$i=1,\ldots,m$">;
</LI>
<LI>prolongation and sum of the <IMG
WIDTH="22" HEIGHT="31" ALIGN="MIDDLE" BORDER="0"
WIDTH="23" HEIGHT="31" ALIGN="MIDDLE" BORDER="0"
SRC="img32.png"
ALT="$w_i$">'s, i.e. <!-- MATH
$w = \sum_{i=1}^m (R_i^{\delta})^T w_i$
@ -309,7 +309,7 @@ time on parallel distributed-memory computers is the so-called <I>Restricted AS
HREF="node24.html#CAI_SARKIS">5</A>,<A
HREF="node24.html#EFSTATHIOU">13</A>]. It
is obtained by zeroing the components of <IMG
WIDTH="22" HEIGHT="31" ALIGN="MIDDLE" BORDER="0"
WIDTH="23" HEIGHT="31" ALIGN="MIDDLE" BORDER="0"
SRC="img32.png"
ALT="$w_i$"> corresponding to the
overlapping vertices when applying the prolongation. Therefore,
@ -613,7 +613,7 @@ M_{2LH-POST}^{-1} = M_{1L}^{-1} + \left( I - M_{1L}^{-1}A \right) M_{C}^{-1}.
-->
<IMG
WIDTH="316" HEIGHT="33" BORDER="0"
WIDTH="317" HEIGHT="33" BORDER="0"
SRC="img58.png"
ALT="\begin{displaymath}
M_{2LH-POST}^{-1} = M_{1L}^{-1} + \left( I - M_{1L}^{-1}A \right) M_{C}^{-1}.

@ -142,7 +142,7 @@ N_r = \left\{s \in W: |a_{rs}| > \theta \sqrt{|a_{rr}a_{ss}|} \right\}
-->
<IMG
WIDTH="320" HEIGHT="38" BORDER="0"
WIDTH="319" HEIGHT="38" BORDER="0"
SRC="img71.png"
ALT="\begin{displaymath}N_r = \left\{s \in W: \vert a_{rs}\vert &gt; \theta \sqrt{\vert a_{rr}a_{ss}\vert} \right\}
\cup \left\{ r \right\} ,
@ -272,7 +272,7 @@ S = I - \omega D^{-1} A ,
-->
<TABLE WIDTH="100%" ALIGN="CENTER">
<TR VALIGN="MIDDLE"><TD ALIGN="CENTER" NOWRAP><A NAME="eq:jac_smoother"></A><IMG
WIDTH="125" HEIGHT="30" BORDER="0"
WIDTH="126" HEIGHT="30" BORDER="0"
SRC="img83.png"
ALT="\begin{displaymath}
S = I - \omega D^{-1} A ,

@ -75,7 +75,7 @@ Ax=b,
-->
<TABLE WIDTH="100%" ALIGN="CENTER">
<TR VALIGN="MIDDLE"><TD ALIGN="CENTER" NOWRAP><A NAME="system1"></A><IMG
WIDTH="58" HEIGHT="30" BORDER="0"
WIDTH="57" HEIGHT="30" BORDER="0"
SRC="img1.png"
ALT="\begin{displaymath}
Ax=b,

@ -0,0 +1,386 @@
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# @echo "FIGURES = $(FIGURES)"
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@if test ! -d tmp; then mkdir tmp; fi
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define latex
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cd tmp; $(MAKEIDX) $(GXS) -o $(GLX) -s glosstex.ist
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define makeindex
@echo
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@echo -n "Starting: "; date
@echo
cd tmp; mv $(IDX) $(IDX)-; $(CLEANIDX) < $(IDX)- > $(IDX)
cd tmp; $(MAKEIDX) $(IDX)
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#----------------------------------------------------------------------------
define finish
@ln -sf tmp/$@ .
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@echo -n "Start: "$(START); echo
@echo -n "Finish: "; date
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endef
define ltx2html-finish
@ln -sf tmp/$@ .
@echo
@echo "----- finish ---------------------------------------------------------"
@echo -n "Start: "$(START); echo
@echo -n "Finish: "; date
@echo -n "Output: "; ls -l -o tmp/$@
@echo -n "Target: "
endef

@ -0,0 +1,27 @@
\section*{Abstract}
\addcontentsline{toc}{section}{Abstract}
\textsc{MLD2P4 (Multi-Level Domain Decomposition Parallel Preconditioners Package based on
PSBLAS}) is a package of parallel algebraic multi-level preconditioners.
It implements various versions of one-level additive and of multi-level additive
and hybrid Schwarz algorithms. In the multi-level case, a purely algebraic approach
is applied to generate coarse-level corrections, so that no geometric background is needed
concerning the matrix to be preconditioned. The matrix is required to be square, real
or complex, with a symmetric sparsity pattern.
MLD2P4 has been designed to provide scalable and easy-to-use preconditioners in the
context of the PSBLAS (Parallel Sparse Basic Linear Algebra Subprograms)
computational framework and can be used in conjuction with the Krylov solvers
available in this framework. MLD2P4 enables the user to easily specify different aspects
of a generic algebraic multilevel Schwarz preconditioner, thus allowing to search
for the ``best'' preconditioner for the problem at hand.
The package has been designed employing object-oriented techniques,
using Fortran 95, with interfaces to additional third party libraries
such as UMFPACK, SuperLU and SuperLU\_Dist, that
can be exploited in building multi-level preconditioners. The parallel
implementation is based on a Single Program Multiple Data (SPMD)
paradigm for distributed-memory architectures; the inter-process data
communication is based on MPI and is managed mainly through PSBLAS.
This guide provides a brief description of the functionalities and
the user interface of MLD2P4.

@ -0,0 +1,348 @@
\section{Multi-level Domain Decomposition Background\label{sec:background}}
\markboth{\textsc{MLD2P4 User's and Reference Guide}}
{\textsc{\ref{sec:background} Multi-level Domain Decomposition Background}}
\emph{Domain Decomposition} (DD) preconditioners, coupled with Krylov iterative
solvers, are widely used in the parallel solution of large and sparse linear systems.
These preconditioners are based on the divide and conquer technique: the matrix
to be preconditioned is divided into submatrices, a ``local'' linear system
involving each submatrix is (approximately) solved, and the local solutions are used
to build a preconditioner for the whole original matrix. This process
often corresponds to dividing a physical domain associated to the original matrix
into subdomains, e.g. in a PDE discretization, to (approximately) solving the
subproblems corresponding to the subdomains and to building an approximate
solution of the original problem from the local solutions
\cite{Cai_Widlund_92,dd1_94,dd2_96}.
\emph{Additive Schwarz} preconditioners are DD preconditioners using overlapping
submatrices, i.e.\ with some common rows, to couple the local information
related to the submatrices (see, e.g., \cite{dd2_96}).
The main motivation for choosing Additive Schwarz preconditioners is their
intrinsic parallelism. A drawback of these
preconditioners is that the number of iterations of the preconditioned solvers
generally grows with the number of submatrices. This may be a serious limitation
on parallel computers, since the number of submatrices usually matches the number
of available processors. Optimal convergence rates, i.e.\ iteration numbers
independent of the number of submatrices, can be obtained by correcting the
preconditioner through a suitable approximation of the original linear system
in a coarse space, which globally couples the information related to the single
submatrices.
\emph{Two-level Schwarz} preconditioners are obtained
by combining basic (one-level) Sch\-warz preconditioners with a coarse-level
correction. In this context, the one-level preconditioner is often
called `smoother'. Different two-level preconditioners are obtained by varying the
choice of the smoother and of the coarse-level correction, and the
way they are combined \cite{dd2_96}. The same reasoning can be applied starting
from the coarse-level system, i.e.\ a coarse-space correction can be built
from this system, thus obtaining \emph{multi-level} preconditioners.
It is worth noting that optimal preconditioners do not necessarily correspond
to minimum execution times. Indeed, to obtain effective multi-level preconditioners
a tradeoff between optimality of convergence and the cost of building and applying
the coarse-space corrections must be achieved. The choice of the number of levels,
i.e.\ of the coarse-space corrections, also affects the effectiveness of the
preconditioners. One more goal is to get convergence rates as less sensitive
as possible to variations in the matrix coefficients.
Two main approaches can be used to build coarse-space corrections. The geometric approach
applies coarsening strategies based on the knowledge of some physical grid associated
to the matrix and requires the user to define grid transfer operators from the fine
to the coarse levels and vice versa. This may result difficult for complex geometries;
furthermore, suitable one-level preconditioners may be required to get efficient
interplay between fine and coarse levels, e.g.\ when matrices with highly varying coefficients
are considered. The algebraic approach builds coarse-space corrections using only matrix
information. It performs a fully automatic coarsening and enforces the interplay between
the fine and coarse levels by suitably choosing the coarse space and the coarse-to-fine
interpolation \cite{StubenGMD69_99}.
MLD2P4 uses a pure algebraic approach for building the sequence of coarse matrices
starting from the original matrix. The algebraic approach is based on the \emph{smoothed
aggregation} algorithm \cite{BREZINA_VANEK,VANEK_MANDEL_BREZINA}. A decoupled version
of this algorithm is implemented, where the smoothed aggregation is applied locally
to each submatrix \cite{TUMINARO_TONG}. In the next two subsections we provide
a brief description of the multi-level Schwarz preconditioners and of the smoothed
aggregation technique as implemented in MLD2P4. For further details the user
is referred to \cite{para_04,aaecc_07,apnum_07,dd2_96}.
\subsection{Multi-level Schwarz Preconditioners\label{sec:multilevel}}
The Multilevel preconditioners implemented in MLD2P4 are obtained by combining
AS preconditioners with coarse-space corrections; therefore
we first provide a sketch of the AS preconditioners.
Given the linear system \Ref{system1},
where $A=(a_{ij}) \in \Re^{n \times n}$ is a
nonsingular sparse matrix with a symmetric nonzero pattern,
let $G=(W,E)$ be the adjacency graph of $A$, where $W=\{1, 2, \ldots, n\}$
and $E=\{(i,j) : a_{ij} \neq 0\}$ are the vertex set and the edge set of $G$,
respectively. Two vertices are called adjacent if there is an edge connecting
them. For any integer $\delta > 0$, a $\delta$-overlap
partition of $W$ can be defined recursively as follows.
Given a 0-overlap (or non-overlapping) partition of $W$,
i.e.\ a set of $m$ disjoint nonempty sets $W_i^0 \subset W$ such that
$\cup_{i=1}^m W_i^0 = W$, a $\delta$-overlap
partition of $W$ is obtained by considering the sets
$W_i^\delta \supset W_i^{\delta-1}$ obtained by including the vertices that
are adjacent to any vertex in $W_i^{\delta-1}$.
Let $n_i^\delta$ be the size of $W_i^\delta$ and $R_i^{\delta} \in
\Re^{n_i^\delta \times n}$ the restriction operator that maps
a vector $v \in \Re^n$ onto the vector $v_i^{\delta} \in \Re^{n_i^\delta}$
containing the components of $v$ corresponding to the vertices in
$W_i^\delta$. The transpose of $R_i^{\delta}$ is a
prolongation operator from $\Re^{n_i^\delta}$ to $\Re^n$.
The matrix $A_i^\delta=R_i^\delta A (R_i^\delta)^T \in
\Re^{n_i^\delta \times n_i^\delta}$ can be considered
as a restriction of $A$ corresponding to the set $W_i^{\delta}$.
The \emph{classical one-level AS} preconditioner is defined by
\[
M_{AS}^{-1}= \sum_{i=1}^m (R_i^{\delta})^T
(A_i^\delta)^{-1} R_i^{\delta},
\]
where $A_i^\delta$ is assumed to be nonsingular. Its application
to a vector $v \in \Re^n$ within a Krylov solver requires the following
three steps:
\begin{enumerate}
\item restriction of $v$ as $v_i = R_i^{\delta} v$, $i=1,\ldots,m$;
\item solution of the linear systems $A_i^\delta w_i = v_i$,
$i=1,\ldots,m$;
\item prolongation and sum of the $w_i$'s, i.e. $w = \sum_{i=1}^m (R_i^{\delta})^T w_i$.
\end{enumerate}
Note that the linear systems at step 2 are usually solved approximately,
e.g.\ using incomplete LU factorizations such as ILU($p$), MILU($p$) and
ILU($p,t$) \cite[Chapter 10]{Saad_book}.
A variant of the classical AS preconditioner that outperforms it
in terms of convergence rate and of computation and communication
time on parallel distributed-memory computers is the so-called \emph{Restricted AS
(RAS)} preconditioner~\cite{CAI_SARKIS,EFSTATHIOU}. It
is obtained by zeroing the components of $w_i$ corresponding to the
overlapping vertices when applying the prolongation. Therefore,
RAS differs from classical AS by the prolongation operators,
which are substituted by $(\tilde{R}_i^0)^T \in \Re^{n_i^\delta \times n}$,
where $\tilde{R}_i^0$ is obtained by zeroing the rows of $R_i^\delta$
corresponding to the vertices in $W_i^\delta \backslash W_i^0$:
\[
M_{RAS}^{-1}= \sum_{i=1}^m (\tilde{R}_i^0)^T
(A_i^\delta)^{-1} R_i^{\delta}.
\]
Analogously, the AS variant called \emph{AS with Harmonic extension (ASH)}
is defined by
\[ M_{ASH}^{-1}= \sum_{i=1}^m (R_i^{\delta})^T
(A_i^\delta)^{-1} \tilde{R}_i^0.
\]
We note that for $\delta=0$ the three variants of the AS preconditioner are
all equal to the block-Jacobi preconditioner.
As already observed, the convergence rate of the one-level Schwarz
preconditioned iterative solvers deteriorates as the number $m$ of partitions
of $W$ increases \cite{dd1_94,dd2_96}. To reduce the dependency
of the number of iterations on the degree of parallelism we may
introduce a global coupling among the overlapping partitions by defining
a coarse-space approximation $A_C$ of the matrix $A$.
In a pure algebraic setting, $A_C$ is usually built with
a Galerkin approach. Given a set $W_C$ of \emph{coarse vertices},
with size $n_C$, and a suitable restriction operator
$R_C \in \Re^{n_C \times n}$, $A_C$ is defined as
\[
A_C=R_C A R_C^T
\]
and the coarse-level correction matrix to be combined with a generic
one-level AS preconditioner $M_{1L}$ is obtained as
\[
M_{C}^{-1}= R_C^T A_C^{-1} R_C,
\]
where $A_C$ is assumed to be nonsingular. The application of $M_{C}^{-1}$
to a vector $v$ corresponds to a restriction, a solution and
a prolongation step; the solution step, involving the matrix $A_C$,
may be carried out also approximately.
The combination of $M_{C}$ and $M_{1L}$ may be
performed in either an additive or a multiplicative framework.
In the former case, the \emph{two-level additive} Schwarz preconditioner
is obtained:
\[
M_{2LA}^{-1} = M_{C}^{-1} + M_{1L}^{-1}.
\]
Applying $M_{2L-A}^{-1}$ to a vector $v$ within a Krylov solver
corresponds to applying $M_{C}^{-1}$
and $M_{1L}^{-1}$ to $v$ independently and then summing up
the results.
In the multiplicative case, the combination can be
performed by first applying the smoother $M_{1L}^{-1}$ and then
the coarse-level correction operator $M_{C}^{-1}$:
\[
\begin{array}{l}
w = M_{1L}^{-1} v, \\
z = w + M_{C}^{-1} (v-Aw);
\end{array}
\]
this corresponds to the following \emph{two-level hybrid pre-smoothed}
Schwarz preconditioner:
\[
M_{2LH-PRE}^{-1} = M_{C}^{-1} + \left( I - M_{C}^{-1}A \right) M_{1L}^{-1}.
\]
On the other hand, by applying the smoother after the coarse-level correction,
i.e.\ by computing
\[
\begin{array}{l}
w = M_{C}^{-1} v , \\
z = w + M_{1L}^{-1} (v-Aw) ,
\end{array}
\]
the \emph{two-level hybrid post-smoothed}
Schwarz preconditioner is obtained:
\[
M_{2LH-POST}^{-1} = M_{1L}^{-1} + \left( I - M_{1L}^{-1}A \right) M_{C}^{-1}.
\]
One more variant of two-level hybrid preconditioner is obtained by applying
the smoother before and after the coarse-level correction. In this case, the
preconditioner is symmetric if $A$, $M_{1L}$ and $M_{C}$ are symmetric.
As previously noted, on parallel computers the number of submatrices usually matches
the number of available processors. When the size of the system to be preconditioned
is very large, the use of many processors, i.e.\ of many small submatrices, often
leads to a large coarse-level system, whose solution may be computationally expensive.
On the other hand, the use of few processors often leads to local sumatrices that
are too expensive to be processed on single processors, because of memory and/or
computing requirements. Therefore, it seems natural to use a recursive approach,
in which the coarse-level correction is re-applied starting from the current
coarse-level system. The corresponding preconditioners, called \emph{multi-level}
preconditioners, can significantly reduce the computational cost of preconditioning
with respect to the two-level case (see \cite[Chapter 3]{dd2_96}).
Additive and hybrid multilevel preconditioners
are obtained as direct extensions of the two-level counterparts.
For a detailed descrition of them, the reader is
referred to \cite[Chapter 3]{dd2_96}.
The algorithm for the application of a multi-level hybrid
post-smoothed preconditioner $M$ to a vector $v$, i.e.\ for the
computation of $w=M^{-1}v$, is reported, for
example, in Figure~\ref{fig:mlhpost_alg}. Here the number of levels
is denoted by $nlev$ and the levels are numbered in increasing order starting
from the finest one, i.e.\ the finest level is level 1; the coarse matrix
and the corresponding basic preconditioner at each level $l$ are denoted by $A_l$ and
$M_l$, respectively, with $A_1=A$.
%
\begin{figure}[t]
\begin{center}
\framebox{
\begin{minipage}{.85\textwidth} {\small
\begin{tabbing}
\quad \=\quad \=\quad \=\quad \\[-1mm]
%
%! assign the finest matrix\\
%$A_1 \leftarrow A$;\\[1mm]
%! define the number of levels $nlev$ \\[1mm]
%! define $nlev-1$ prolongators\\
%$R_l^T, l=2, \ldots, nlev$;\\[1mm]
%! define $nlev-1$ coarser matrices\\
%$A_l \leftarrow R_lA_{l-1}R_l^T, \; l=2, \ldots, nlev$;\\[1mm]
%! define the $nlev-1$ basic Schwarz preconditioners\\
%$M_l$, basic preconditioner for $A_l \; l=1, \ldots, nlev-1$;\\[1mm]
%$! assign a vector $v$\\
%
$v_1 = v$; \\[2mm]
\textbf{for $l=2, nlev$ do}\\[1mm]
\> ! transfer $v_{l-1}$ to the next coarser level\\
\> $v_l = R_lv_{l-1}$ \\[1mm]
\textbf{endfor} \\[2mm]
! apply the coarsest-level correction\\[1mm]
$y_{nlev} = A_{nlev}^{-1} v_{nlev}$\\[2mm]
\textbf{for $l=nlev -1 , 1, -1$ do}\\[1mm]
\> ! transfer $y_{l+1}$ to the next finer level\\
\> $y_l = R_{l+1}^T y_{l+1}$;\\[1mm]
\> ! compute the residual at the current level\\
\> $r_l = v_l-A_l^{-1} y_l$;\\[1mm]
\> ! apply the basic Schwarz preconditioner to the residual\\
\> $r_l = M_l^{-1} r_l$\\[1mm]
\> ! update $y_l$\\
\> $y_l = y_l+r_l$\\
\textbf{endfor} \\[1mm]
$w = y_1$;
\end{tabbing}
}
\end{minipage}
}
\caption{Application of the multi-level hybrid post-smoothed preconditioner.\label{fig:mlhpost_alg}}
\end{center}
\end{figure}
%
\subsection{Smoothed Aggregation\label{sec:aggregation}}
In order to define the restriction operator $R_C$, which is used to compute
the coarse-level matrix $A_C$, MLD2P4 uses the \emph{smoothed aggregation}
algorithm described in \cite{BREZINA_VANEK,VANEK_MANDEL_BREZINA}.
The basic idea of this algorithm is to build a coarse set of vertices
$W_C$ by suitably grouping the vertices of $W$ into disjoint subsets
(aggregates), and to define the coarse-to-fine space transfer operator $R_C^T$ by
applying a suitable smoother to a simple piecewise constant
prolongation operator, to improve the quality of the coarse-space correction.
Three main steps can be identified in the smoothed aggregation procedure:
\begin{enumerate}
\item coarsening of the vertex set $W$, to obtain $W_C$;
\item construction of the prolongator $R_C^T$;
\item application of $R_C$ and $R_C^T$ to build $A_C$.
\end{enumerate}
%\textbf{NOTA: Controllare cosa fa trilinos dopo il primo passo.}
To perform the coarsening step, we have implemented the aggregation algorithm sketched
in \cite{apnum_07}. According to \cite{VANEK_MANDEL_BREZINA}, a modification of
this algorithm has been actually considered,
in which each aggregate $N_r$ is made of vertices of $W$ that are \emph{strongly coupled}
to a certain root vertex $r \in W$, i.e.\
\[ N_r = \left\{s \in W: |a_{rs}| > \theta \sqrt{|a_{rr}a_{ss}|} \right\}
\cup \left\{ r \right\} ,
\]
for a given $\theta \in [0,1]$.
Since this algorithm has a sequential nature, a \emph{decoupled} version of
it has been chosen, where each processor $i$ independently applies the algorithm to
the set of vertices $W_i^0$ assigned to it in the initial data distribution. This
version is embarrassingly parallel, since it does not require any data communication.
On the other hand, it may produce non-uniform aggregates near boundary vertices,
i.e.\ near vertices adjacent to vertices in other processors, and is strongly
dependent on the number of processors and on the initial partitioning of the matrix $A$.
Nevertheless, this algorithm has been chosen for the implementation in MLD2P4,
since it has been shown to produce good results in practice
\cite{aaecc_07,apnum_07,TUMINARO_TONG}.
The prolongator $P_C=R_C^T$ is built starting from a \emph{tentative prolongator}
$P \in \Re^{n \times n_C}$, defined as
\begin{equation}
P=(p_{ij}), \quad p_{ij}=
\left\{ \begin{array}{ll}
1 & \quad \mbox{if} \; i \in V^j_C \\
0 & \quad \mbox{otherwise}
\end{array} \right. .
\label{eq:tent_prol}
\end{equation}
$P_C$ is obtained by
applying to $P$ a smoother $S \in \Re^{n \times n}$:
\begin{equation}
P_C = S P,
\label{eq:smoothed_prol}
\end{equation}
in order to remove oscillatory components from the range of the prolongator
and hence to improve the convergence properties of the multi-level
Schwarz method \cite{BREZINA_VANEK,StubenGMD69_99}.
A simple choice for $S$ is the damped Jacobi smoother:
\begin{equation}
S = I - \omega D^{-1} A ,
\label{eq:jac_smoother}
\end{equation}
where the value of $\omega$ can be chosen
using some estimate of the spectral radius of $D^{-1}A$ \cite{BREZINA_VANEK}.
%
%\textbf{NOTA: filtering di $A$ nello smoothing, da implementare?}
%
%%% Local Variables:
%%% mode: latex
%%% TeX-master: "userguide"
%%% End:

@ -0,0 +1,207 @@
%\section{Bibliography\label{sec:bib}}
\begin{thebibliography}{99}
\addcontentsline{toc}{section}{\refname}
\markboth{\textsc{MLD2P4 User's and Reference Guide}}
{\textsc{References}}
%\let\refname\relax
%
%\bibitem{PARA04FOREST}
%G.~Bella, S.~Filippone, A.~De Maio, A., Testa, M.:
%A Simulation Model for Forest Fires.
%In: Dongarra, J., Madsen, K., Wasniewski, J. (eds.):
%Proceedings of PARA~04 Workshop on State of the Art
%in Scientific Computing. Lecture Notes in Computer Science, 3732. Berlin:
%Springer, 2005
%
\bibitem{BREZINA_VANEK}
M.~Brezina, P.~Van{\v e}k,
{\em A Black-Box Iterative Solver Based on a Two-Level Schwarz Method},
Computing, 63, 1999, 233--263.
%
\bibitem{para_04}
A.~Buttari, P.~D'Ambra, D.~di Serafino, S.~Filippone,
{\em Extending PSBLAS to Build Parallel Schwarz Preconditioners},
in , J.~Dongarra, K.~Madsen, J.~Wasniewski, editors,
Proceedings of PARA~04 Workshop on State of the Art
in Scientific Computing, Lecture Notes in Computer Science,
Springer, 2005, 593--602.
%
\bibitem{aaecc_07} A.~Buttari, P.~D'Ambra, D.~di~Serafino, S.~Filippone,
{\em 2LEV-D2P4: a package of high-performance preconditioners
for scientific and engineering applications},
Applicable Algebra in Engineering, Communications and Computing,
18, 3, 2007, 223--239.
%Published online: 13 February 2007, {\tt http://dx.doi.org/10.1007/s00200-007-0035-z}
%
\bibitem{apnum_07} P.~D'Ambra, S.~Filippone, D.~di~Serafino,
{\em On the Development of PSBLAS-based Parallel Two-level Schwarz Preconditioners},
Applied Numerical Mathematics, Elsevier Science,
57, 11-12, 2007, 1181-1196.
%published online 3 February 2007, {\tt
% http://dx.doi.org/10.1016/j.apnum.2007.01.006}
%% \bibitem{DOUGLAS}
%% R.E.~Bank and C.C.~Douglas,
%% {\em SMMP: Sparse Matrix Multiplication Package},
%% Advances in Computational Mathematics, 1993, 1, 127-137.
%% (See also {\tt http://www.mgnet.org/~douglas/ccd-codes.html})
%
%
%% \bibitem{CAI_SAAD}
%% X.~C.~Cai and Y.~Saad,
%% {\em Overlapping Domain Decomposition Algorithms for General Sparse Matrices},
%% Numerical Linear Algebra with Applications, 3(3), pp.~221--237, 1996.
%
\bibitem{CAI_SARKIS}
X.~C.~Cai, M.~Sarkis,
{\em A Restricted Additive Schwarz Preconditioner for General Sparse Linear Systems},
SIAM Journal on Scientific Computing, 21, 2, 1999, 792--797.
%
\bibitem{Cai_Widlund_92}
X.~C.~Cai, O.~B.~Widlund,
{\em Domain Decomposition Algorithms for Indefinite Elliptic Problems},
SIAM Journal on Scientific and Statistical Computing, 13, 1, 1992, 243--258.
%
\bibitem{dd1_94}
T.~Chan and T.~Mathew,
{\em Domain Decomposition Algorithms},
in A.~Iserles, editor, Acta Numerica 1994, 61--143.
Cambridge University Press.
%
\bibitem{UMFPACK}
T.A.~Davis,
{\em Algorithm 832: UMFPACK - an Unsymmetric-pattern Multifrontal
Method with a Column Pre-ordering Strategy},
ACM Transactions on Mathematical Software, 30, 2004, 196--199.
(See also {\tt http://www.cise.ufl.edu/~davis/})
%
\bibitem{SUPERLU}
J.W.~Demmel, S.C.~Eisenstat, J.R.~Gilbert, X.S.~Li and J.W.H.~Liu,
A supernodal approach to sparse partial pivoting,
SIAM Journal on Matrix Analysis and Applications, 20, 3, 1999, 720--755.
%
\bibitem{blas3}
J.~J.~Dongarra, J.~Du Croz, I.~S.~Duff, S.~Hammarling,
\emph{A set of Level 3 Basic Linear Algebra Subprograms},
ACM Transactions on Mathematical Software, 16, 1990, 1--17.
%
\bibitem{blas2}
J.~J.~Dongarra, J.~Du Croz, S.~Hammarling, R.~J.~Hanson,
\emph{An extended set of FORTRAN Basic Linear Algebra Subprograms},
ACM Transactions on Mathematical Software, 14, 1988, 1--17.
%
\bibitem{BLACS}
J.~J.~Dongarra and R.~C.~Whaley,
{\em A User's Guide to the BLACS v.~1.1},
Lapack Working Note 94, Tech.\ Rep.\ UT-CS-95-281, University of
Tennessee, March 1995 (updated May 1997).
%
%\bibitem{sblas_97}
%I.~Duff, M.~Marrone, G.~Radicati and C.~Vittoli,
%{\em Level 3 Basic Linear Algebra Subprograms for Sparse Matrices:
%a User Level Interface},
%ACM Transactions on Mathematical Software, 23(3), pp.~379--401, 1997.
%
%\bibitem{sblas_02}
%I.~Duff, M.~Heroux and R.~Pozo,
%{\em An Overview of the Sparse Basic Linear
%Algebra Subprograms: the New Standard from the BLAS Technical Forum},
%ACM Transactions on Mathematical Software, 28(2), pp.~239--267, 2002.
%
\bibitem{EFSTATHIOU}
E.~Efstathiou, J.~G.~Gander,
{\em Why Restricted Additive Schwarz Converges Faster than Additive Schwarz},
BIT Numerical Mathematics, 43, 2003, 945--959.
%
\bibitem{PSBLASGUIDE}
S.~Filippone, A.~Buttari,
{\em PSBLAS-2.3 User's Guide. A Reference Guide for the Parallel Sparse BLAS Library}, 2008,
available from \texttt{http://www.ce.uniroma2.it/psblas/}.
%
\bibitem{psblas_00}
S.~Filippone, M.~Colajanni,
{\em PSBLAS: A Library for Parallel Linear Algebra
Computation on Sparse Matrices},
ACM Transactions on Mathematical Software, 26, 4, 2000, 527--550.
%
\bibitem{MPI2}
W.~Gropp, S.~Huss-Lederman, A.~Lumsdaine, E.~Lusk, B.~Nitzberg, W.~Saphir, M.~Snir,
{\em MPI: The Complete Reference. Volume 2 - The MPI-2 Extensions},
MIT Press, 1998.
%
\bibitem{blas1}
C.~L.~Lawson, R.~J.~Hanson, D.~Kincaid, F.~T.~Krogh,
\emph{Basic Linear Algebra Subprograms for FORTRAN usage},
ACM Transactions on Mathematical Software, 5, 1979, 308--323.
%
\bibitem{SUPERLUDIST}
X.~S.~Li, J.~W.~Demmel, {\em SuperLU\_DIST: A Scalable Distributed-memory
Sparse Direct Solver for Unsymmetric Linear Systems},
ACM Transactions on Mathematical Software, 29, 2, 2003, 110--140.
%
%\bibitem{KIVA3PSBLAS}
%S.~Filippone, P.~D'Ambra, M.~Colajanni,
%{\em Using a Parallel Library of Sparse Linear Algebra in a Fluid Dynamics
%Applications Code on Linux Clusters},
%in G.~Joubert, A.~Murli, F.~Peters, M.~Vanneschi, editors,
%Parallel Computing - Advances \& Current Issues,
%pp.~441--448, Imperial College Press, 2002.
%
%\bibitem{METIS}
%Karypis, G. and Kumar, V.,
%{\em {METIS}: Unstructured Graph Partitioning and Sparse Matrix
% Ordering System}.
%Minneapolis, MN 55455: University of Minnesota, Department of
% Computer Science, 1995.
%Internet Address: {\verb|http://www.cs.umn.edu/~karypis|}.
%\bibitem{BLAS1}
%Lawson, C., Hanson, R., Kincaid, D. and Krogh, F.,
% Basic {L}inear {A}lgebra {S}ubprograms for {F}ortran usage,
%{ACM Trans. Math. Softw.} vol.~{5}, 38--329, 1979.
%
%\bibitem{machiels}
%{Machiels, L. and Deville, M.}
%{\em Fortran 90: An entry to object-oriented programming for the solution
% of partial differential equations.}
%{ACM Trans. Math. Softw.} vol.~{23}, 32--49.
%\bibitem{metcalf}
%{Metcalf, M., Reid, J. and Cohen, M.}
%{\em Fortran 95/2003 explained.}
%{Oxford University Press}, 2004.
%
\bibitem{Saad_book}
Y.~Saad,
\emph{Iterative methods for sparse linear systems}, 2nd edition,
SIAM, 2003
\bibitem{dd2_96}
B.~Smith, P.~Bjorstad, W.~Gropp,
{\em Domain Decomposition: Parallel Multilevel Methods for Elliptic
Partial Differential Equations},
Cambridge University Press, 1996.
%
\bibitem{MPI1}
M.~Snir, S.~Otto, S.~Huss-Lederman, D.~Walker, J.~Dongarra,
{\em MPI: The Complete Reference. Volume 1 - The MPI Core}, second edition,
MIT Press, 1998.
%%
\bibitem{StubenGMD69_99}
K.~St\"{u}ben,
{\em Algebraic Multigrid (AMG): an Introduction with Applications},
in A.~Sch\"{u}ller, U.~Trottenberg, C.~Oosterlee, editors, Multigrid,
Academic Press, 2000.
%
\bibitem{TUMINARO_TONG}
R.~S.~Tuminaro, C.~Tong,
{\em Parallel Smoothed Aggregation Multigrid: Aggregation Strategies on Massively Parallel Machines},
in J. Donnelley, editor, Proceedings of SuperComputing 2000, Dallas, 2000.
%
\bibitem{VANEK_MANDEL_BREZINA}
P.~Van{\v e}k, J.~Mandel and M.~Brezina,
{\em Algebraic Multigrid by Smoothed Aggregation for Second and Fourth Order Elliptic Problems},
Computing, 56, 1996, 179-196.
%
\end{thebibliography}

@ -0,0 +1,242 @@
\section{Configuring and Building MLD2P4\label{sec:building}}
\markboth{\textsc{MLD2P4 User's and Reference Guide}}
{\textsc{\ref{sec:building} Configuring and Building MLD2P4}}
To build MLD2P4 it is necessary to set up a Makefile with appropriate
values for your system; this is done by means of the \verb|configure|
script. The distribution also includes the autoconf and automake
sources employed to generate the script, but usually this is not needed
to build the software.
MLD2P4 is implemented almost entirely in Fortran~95, with some
interfaces to external libraries in C; the Fortran compiler
must support the Fortran~95 standard plus the extension TR15581, which
enhances the usability of \verb|ALLOCATABLE| variables. Most modern
Fortran compilers support this language level. In particular, this is
supported by the GNU Fortran compiler as of version 4.2.0; however we
recommend to use the latest available release (4.3.1 at the time of
this writing).
The software defines data types and interfaces for
real and complex data, in both single and double precision.
\subsection{Prerequisites}
The following base libraries are needed:
\begin{description}
\item[BLAS] \cite{blas3,blas2,blas1} Many vendors provide optimized versions
of the Basic Linear Algebra Subprograms; if no vendor version is
available for a given platform, the ATLAS software
(\verb!http://math-atlas.sourceforge.net/!)
may be employed. The reference BLAS from Netlib
(\verb|http://www.netlib.org/blas|) are meant to define the standard
behaviour of the BLAS interface, so they are not optimized for any
particular plaftorm, and should only be used as a last
resort. Note that BLAS computations form a relatively small part of
the MLD2P4/PSBLAS computations; they are however critical when using
preconditioners based on the UMFPACK or SuperLU third party
libraries.
\item[MPI] \cite{MPI2,MPI1} A version of MPI is available on most
high-performance computing systems; only version 1.1 is required.
\item[BLACS] \cite{BLACS} The Basic Linear Algebra Communication Subprograms
are available in source form from \verb|http://www.netlib.org/blacs|;
some vendors include them in their parallel computing
support libraries.
\item[PSBLAS] \cite{PSBLASGUIDE,psblas_00} Parallel Sparse BLAS is
available from \\ \verb|http://www.ce.uniroma2.it/psblas|; version 2.3
(or later) is required. Indeed, all the prerequisites
listed so far are also prerequisites of PSBLAS.
To build the MLD2P4 library it is necessary to get access to
the source PSBLAS directory employed to build the version under use; after
the MLD2P4 build process completes, only the compiled form of the
PSBLAS library is necessary to build user applications.
\end{description}
Please note that the four previous libraries must have Fortran
interfaces compatible with MLD2P4;
usually this means that they should all be built with the same
compiler as MLD2P4.
\subsection{Optional third party libraries}
We provide interfaces to the following third-party software libraries;
note that these are optional, but if you enable them some defaults
for multilevel preconditioners may change to reflect their presence.
\begin{description}
\item[UMFPACK] \cite{UMFPACK}
A sparse direct factorization package available from \\
\verb|http://www.cise.ufl.edu/research/sparse/umfpack/|;
provides serial factorization and triangular system solution for double
precision real and complex data. We have tested
versions 4.4 and 5.1.
\item[SuperLU] \cite{SUPERLU}
A sparse direct factorization package available from \\
\verb|http://crd.lbl.gov/~xiaoye/SuperLU/|; provides serial
factorization and triangular system solution for single and double precision,
real and complex data. We have tested versions 3.0 and 3.1.
\item[SuperLU\_Dist] \cite{SUPERLUDIST}
A sparse direct factorization package available
from the same site as SuperLU; provides parallel factorization and
triangular system solution for double precision real and complex data.
We have tested version 2.1.
\end{description}
\subsection{Configuration options}
To build MLD2P4 the first step is to use the \verb|configure| script
in the main directory to generate the necessary makefile(s).
As a minimal example consider the following:
\begin{verbatim}
./configure --with-psblas=/home/user/PSBLAS/psblas-2.3
\end{verbatim}
which assumes that the various MPI compilers and support libraries are
available in the standard directories on the system, and specifies
only the PSBLAS build directory (note that the latter directory must
be specified with an {\em absolute} path).
The full set of options may be looked at by issuing the command
\verb|./configure --help|, which produces:
\begin{verbatim}
`configure' configures MLD2P4 1.0 to adapt to many kinds of systems.
Usage: ./configure [OPTION]... [VAR=VALUE]...
To assign environment variables (e.g., CC, CFLAGS...), specify them as
VAR=VALUE. See below for descriptions of some of the useful variables.
Defaults for the options are specified in brackets.
Configuration:
-h, --help display this help and exit
--help=short display options specific to this package
--help=recursive display the short help of all the included packages
-V, --version display version information and exit
-q, --quiet, --silent do not print `checking...' messages
--cache-file=FILE cache test results in FILE [disabled]
-C, --config-cache alias for `--cache-file=config.cache'
-n, --no-create do not create output files
--srcdir=DIR find the sources in DIR [configure dir or `..']
Installation directories:
--prefix=PREFIX install architecture-independent files in PREFIX
[/usr/local]
--exec-prefix=EPREFIX install architecture-dependent files in EPREFIX
[PREFIX]
By default, `make install' will install all the files in
`/usr/local/bin', `/usr/local/lib' etc. You can specify
an installation prefix other than `/usr/local' using `--prefix',
for instance `--prefix=$HOME'.
For better control, use the options below.
Fine tuning of the installation directories:
--bindir=DIR user executables [EPREFIX/bin]
--sbindir=DIR system admin executables [EPREFIX/sbin]
--libexecdir=DIR program executables [EPREFIX/libexec]
--sysconfdir=DIR read-only single-machine data [PREFIX/etc]
--sharedstatedir=DIR modifiable architecture-independent data [PREFIX/com]
--localstatedir=DIR modifiable single-machine data [PREFIX/var]
--libdir=DIR object code libraries [EPREFIX/lib]
--includedir=DIR C header files [PREFIX/include]
--oldincludedir=DIR C header files for non-gcc [/usr/include]
--datarootdir=DIR read-only arch.-independent data root [PREFIX/share]
--datadir=DIR read-only architecture-independent data [DATAROOTDIR]
--infodir=DIR info documentation [DATAROOTDIR/info]
--localedir=DIR locale-dependent data [DATAROOTDIR/locale]
--mandir=DIR man documentation [DATAROOTDIR/man]
--docdir=DIR documentation root [DATAROOTDIR/doc/mld2p4]
--htmldir=DIR html documentation [DOCDIR]
--dvidir=DIR dvi documentation [DOCDIR]
--pdfdir=DIR pdf documentation [DOCDIR]
--psdir=DIR ps documentation [DOCDIR]
Optional Packages:
--with-PACKAGE[=ARG] use PACKAGE [ARG=yes]
--without-PACKAGE do not use PACKAGE (same as --with-PACKAGE=no)
--with-psblas The source directory for PSBLAS, for example,
--with-psblas=/opt/packages/psblas-2.3
--with-libs List additional link flags here. For example,
--with-libs=-lspecial_system_lib or
--with-libs=-L/path/to/libs
--with-clibs additional CLIBS flags to be added: will prepend
to CLIBS
--with-flibs additional FLIBS flags to be added: will prepend
to FLIBS
--with-library-path additional LIBRARYPATH flags to be added: will
prepend to LIBRARYPATH
--with-include-path additional INCLUDEPATH flags to be added: will
prepend to INCLUDEPATH
--with-module-path additional MODULE_PATH flags to be added: will
prepend to MODULE_PATH
--with-umfpack=LIBNAME Specify the library name for UMFPACK library.
Default: "-lumfpack -lamd"
--with-umfpackdir=DIR Specify the directory for UMFPACK library and
includes.
--with-superlu=LIBNAME Specify the library name for SUPERLU library.
Default: "-lslu"
--with-superludir=DIR Specify the directory for SUPERLU library and
includes.
--with-superludist=LIBNAME
Specify the libname for SUPERLUDIST library.
Requires you also specify SuperLU. Default: "-lslud"
--with-superludistdir=DIR
Specify the directory for SUPERLUDIST library and
includes.
Some influential environment variables:
FC Fortran compiler command
FCFLAGS Fortran compiler flags
LDFLAGS linker flags, e.g. -L<lib dir> if you have libraries in a
nonstandard directory <lib dir>
LIBS libraries to pass to the linker, e.g. -l<library>
CC C compiler command
CFLAGS C compiler flags
CPPFLAGS C/C++/Objective C preprocessor flags, e.g. -I<include dir> if
you have headers in a nonstandard directory <include dir>
CPP C preprocessor
MPICC MPI C compiler command
Use these variables to override the choices made by `configure' or to help
it to find libraries and programs with nonstandard names/locations.
Report bugs to <bugreport@mld2p4.it>.
\end{verbatim}
Thus, a sample build with libraries in installation
directories specifics to the GNU 4.3 compiler suite might be as
follows, specifying only the UMFPACK external package:
\begin{verbatim}
./configure --with-psblas=/home/user/psblas-2.3/ \
--with-libs="-L/usr/local/BLAS/gnu43 -L/usr/local/BLACS/gnu43" \
--with-blacs=-lmpiblacs --with-umfpackdir=/usr/local/UMFPACK/gnu43
\end{verbatim}
Once the configure script has completed execution, it will have
generated the file \verb|Make.inc| which will then be used by all
Makefiles in the directory tree.
To build the library the user will now enter
\begin{verbatim}
make
\end{verbatim}
followed (optionally) by
\begin{verbatim}
make install
\end{verbatim}
\subsection{Example and test programs\label{sec:ex_and_test}}
The package contains the \verb|examples| and \verb|tests| directories;
both of them are further divided into \verb|fileread| and
\verb|pargen| subdirectories. Their purpose is as follows:
\begin{description}
\item[\tt examples] contains a set of simple example programs with a
predefined choice of preconditioners, selectable via integer
values. These are intended to get an acquaintance with the
multilevel preconditioners.
\item[\tt tests] contains a set of more sophisticated examples that
will allow the user, via the input files in the \verb|runs|
subdirectories, to experiment with the full range of preconditioners
implemented in the library.
\end{description}
The \verb|fileread| directories contain sample programs that read
sparse matrices from files, according to the Matrix Market or the
Harwell-Boeing storage format; the \verb|pdegen| instead generate
matrices in full parallel mode from the discretization of a sample PDE.

@ -0,0 +1,19 @@
\section{Code Distribution\label{sec:distribution}}
\markboth{\textsc{MLD2P4 User's and Reference Guide}}
{\textsc{\ref{sec:distribution} Code Distribution}}
\noindent
MLD2P4 is available from the web site
\begin{quotation}
\texttt{http://www.mld2p4.it}
\end{quotation}
where contact points for further information can be also found.
To report bugs or ask general usage questions, please, send an email to
\texttt{bugreport@mld2p4.it}.
The software is available under a modified BSD license, as specified
in Appendix~\ref{sec:license}; please note that some of the optional
third party libraries may be licensed under a different and more
stringent license, most notably the GPL, and this should be taken into
account when treating derived works.

@ -0,0 +1,20 @@
\section{Error Handling\label{sec:errors}}
\markboth{\textsc{MLD2P4 User's and Reference Guide}}
{\textsc{\ref{sec:errors} Error handling}}
The error handling in MLD2P4 is based on the PSBLAS (version 2) error
handling. Error conditions are signaled via an integer argument
\verb|info|; whenever an error condition is detected, an error trace
stack is built by the library up to the top-level, user-callable
routine. This routine will then decide, according to the user
preferences, whether the error should be handled by terminating the
program or by returning the error condition to the user code, which
will then take action, and whether
an error message should be printed. These options may be set by using
the PSBLAS error handling routines; for further details see the PSBLAS
User's Guide \cite{PSBLASGUIDE}.
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\section{Getting Started\label{sec:started}}
\markboth{\textsc{MLD2P4 User's and Reference Guide}}
{\textsc{\ref{sec:started} Getting Started}}
We describe the basics for building and applying MLD2P4 one-level and multi-level
Schwarz preconditioners with the Krylov solvers included in PSBLAS \cite{PSBLASGUIDE}.
The following steps are required:
\begin{enumerate}
\item \emph{Declare the preconditioner data structure}. It is a derived data type,
\verb|mld_|\-\emph{x}\verb|prec_| \verb|type|, where \emph{x} may be \verb|s|, \verb|d|, \verb|c|
or \verb|z|, according to the basic data type of the sparse matrix
(\verb|s| = real single precision; \verb|d| = real double precision;
\verb|c| = complex single precision; \verb|z| = complex double precision).
This data structure is accessed by the user only through the MLD2P4 routines,
following an object-oriented approach.
\item \emph{Allocate and initialize the preconditioner data structure, according to
a preconditioner type chosen by the user}. This is performed by the routine
\verb|mld_precinit|, which also sets defaults for each preconditioner
type selected by the user. The defaults associated to each preconditioner
type are given in Table~\ref{tab:precinit}, where the strings used by
\verb|mld_precinit| to identify the preconditioner types are also given.
Note that these strings are valid also if uppercase letters are substituted by
corresponding lowercase ones.
\item \emph{Modify the selected preconditioner type, by properly setting
preconditioner parameters.} This is performed by the routine \verb|mld_precset|.
This routine must be called only if the user wants to modify the default values
of the parameters associated to the selected preconditioner type, to obtain a variant
of the preconditioner. Examples of use of \verb|mld_precset| are given in
Section~\ref{sec:examples}; a complete list of all the
preconditioner parameters and their allowed and default values is provided in
Section~\ref{sec:userinterface}, Tables~\ref{tab:p_type}-\ref{tab:p_coarse}.
\item \emph{Build the preconditioner for a given matrix.} This is performed by
the routine \verb|mld_precbld|.
\item \emph{Apply the preconditioner at each iteration of a Krylov solver.}
This is performed by the routine \verb|mld_precaply|. When using the PSBLAS Krylov solvers,
this step is completely transparent to the user, since \verb|mld_precaply| is called
by the PSBLAS routine implementing the Krylov solver (\verb|psb_krylov|).
\item \emph{Free the preconditioner data structure}. This is performed by
the routine \verb|mld_| \verb|precfree|. This step is complementary to step 1 and should
be performed when the preconditioner is no more used.
\end{enumerate}
A detailed description of the above routines is given in Section~\ref{sec:userinterface}.
Examples showing the basic use of MLD2P4 are reported in Section~\ref{sec:examples}.
Note that the Fortran 95 module \verb|mld_prec_mod|, containing the definition of the
preconditioner data type and the interfaces to the routines of MLD2P4,
must be used in any program calling such routines.
The modules \verb|psb_base_mod|, for the sparse matrix and communication descriptor
data types, and \verb|psb_krylov_mod|, for interfacing with the
Krylov solvers, must be also used (see Section~\ref{sec:examples}).
\ \\
\textbf{Remark 1.} The coarsest-level solver used by the default two-level
preconditioner has been chosen by taking into account that, on parallel
machines, it often leads to the smallest execution time when applied to
linear systems coming from finite-difference discretizations of basic
elliptic PDE problems, considered as standard tests for multi-level Schwarz
preconditioners \cite{aaecc_07,apnum_07}. However, this solver does
not necessarily correspond to the smallest number of iterations of the
preconditioned Krylov method, which is usually obtained by applying
a direct solver to the coarsest-level system, e.g.\ based on the LU
factorization (see Section~\ref{sec:userinterface}
for the coarsest-level solvers available in MLD2P4).
\ \\
\textbf{Remark 2.} The include path for MLD2P4 must override
those for PSBLAS, e.g.\ the latter must come first in the sequence
passed to the compiler, as the MLD2P4 version of the Krylov solver
interfaces must override that of PSBLAS. This will change in the future
when the support for the \verb|class| statement becomes widespread in Fortran
compilers.
\begin{table}[th]
\begin{center}
%{\small
\begin{tabular}{|l|l|p{7.8cm}|}
\hline
\textsc{type} & \textsc{string} & \textsc{default preconditioner} \\ \hline
No preconditioner &\verb|'NOPREC'|& Considered only to use the PSBLAS
Krylov solvers with no preconditioner. \\ \hline
Diagonal & \verb|'DIAG'| & --- \\ \hline
Block Jacobi & \verb|'BJAC'| & Block Jacobi with ILU(0) on the local blocks.\\ \hline
Additive Schwarz & \verb|'AS'| & Restricted Additive Schwarz (RAS),
with overlap 1 and ILU(0) on the local blocks. \\ \hline
Multilevel &\verb|'ML'| & Multi-level hybrid preconditioner (additive on the
same level and multiplicative through the levels),
with post-smoothing only.
Number of levels: 2.
Post-smoother: RAS with overlap 1 and ILU(0)
on the local blocks.
Aggregation: decoupled smoothed aggregation with
threshold $\theta = 0$.
Coarsest matrix: distributed among the processors.
Coarsest-level solver:
4 sweeps of the block-Jacobi solver,
with LU (or ILU) factorization of the blocks
(UMFPACK for the double precision versions and
SuperLU for the single precision ones, if the packages
have been installed; ILU(0), otherwise). \\
\hline
\end{tabular}
%}
\end{center}
\caption{Preconditioner types, corresponding strings and default choices.
\label{tab:precinit}}
\end{table}
\subsection{Examples\label{sec:examples}}
The code reported in Figure~\ref{fig:ex_default} shows how to set and apply the default
multi-level preconditioner available in the real double precision version
of MLD2P4 (see Table~\ref{tab:precinit}). This preconditioner is chosen
by simply specifying \verb|'ML'| as second argument of \verb|mld_precinit|
(a call to \verb|mld_precset| is not needed) and is applied with the BiCGSTAB
solver provided by PSBLAS. As previously observed, the modules \verb|psb_base_mod|,
\verb|mld_prec_mod| and \verb|psb_krylov_mod| must be used by the example program.
The part of the code concerning the
reading and assembling of the sparse matrix and the right-hand side vector, performed
through the PSBLAS routines for sparse matrix and vector management, is not reported
here for brevity; the statements concerning the deallocation of the PSBLAS
data structure are neglected too.
The complete code can be found in the example program file \verb|mld_dexample_ml.f90|,
in the directory \verb|examples/fileread| of the MLD2P4 tree (see
Section~\ref{sec:ex_and_test}).
For details on the use of the PSBLAS routines, see the PSBLAS User's
Guide \cite{PSBLASGUIDE}.
The setup and application of the default multi-level
preconditioners for the real single precision and the complex, single and double
precision, versions are obtained with straightforward modifications of the previous
example (see Section~\ref{sec:userinterface} for details). If these versions are installed,
the corresponding Fortran 95 codes are available in \verb|examples/fileread/|.
\begin{figure}[tbp]
\begin{center}
\begin{minipage}{.90\textwidth}
{\small
\begin{verbatim}
use psb_base_mod
use mld_prec_mod
use psb_krylov_mod
... ...
!
! sparse matrix
type(psb_dspmat_type) :: A
! sparse matrix descriptor
type(psb_desc_type) :: desc_A
! preconditioner
type(mld_dprec_type) :: P
! right-hand side and solution vectors
real(kind(1.d0)) :: b(:), x(:)
... ...
!
! initialize the parallel environment
call psb_init(ictxt)
call psb_info(ictxt,iam,np)
... ...
!
! read and assemble the matrix A and the right-hand side b
! using PSBLAS routines for sparse matrix / vector management
... ...
!
! initialize the default multi-level preconditioner, i.e. hybrid
! Schwarz, using RAS (with overlap 1 and ILU(0) on the blocks)
! as post-smoother and 4 block-Jacobi sweeps (with UMFPACK LU
! on the blocks) as distributed coarse-level solver
call mld_precinit(P,'ML',info)
!
! build the preconditioner
call mld_precbld(A,desc_A,P,info)
!
! set the solver parameters and the initial guess
... ...
!
! solve Ax=b with preconditioned BiCGSTAB
call psb_krylov('BICGSTAB',A,P,b,x,tol,desc_A,info)
... ...
!
! deallocate the preconditioner
call mld_precfree(P,info)
!
! deallocate other data structures
... ...
!
! exit the parallel environment
call psb_exit(ictxt)
stop
\end{verbatim}
}
\end{minipage}
\caption{Setup and application of the default multi-level Schwarz preconditioner.
\label{fig:ex_default}}
\end{center}
\end{figure}
Different versions of multi-level preconditioners can be obtained by changing
the default values of the preconditioner parameters. The code reported in
Figure~\ref{fig:ex_3lh} shows how to set a three-level hybrid Schwarz
preconditioner, which uses block Jacobi with ILU(0) on the
local blocks as post-smoother, has a coarsest matrix replicated on the processors,
and solves the coarsest-level system with the LU factorization from UMFPACK~\cite{UMFPACK}.
The number of levels is specified by using \verb|mld_precinit|; the other
preconditioner parameters are set by calling \verb|mld_precset|. Note that
the type of multilevel framework (i.e.\ multiplicative among the levels
with post-smoothing only) is not specified since it is the default
set by \verb|mld_precinit|.
Figure~\ref{fig:ex_3la} shows how to
set a three-level additive Schwarz preconditioner,
which uses RAS, with overlap 1 and ILU(0) on the blocks,
as pre- and post-smoother, and applies five block-Jacobi sweeps, with
the UMFPACK LU factorization on the blocks, as distributed coarsest-level
solver. Again, \verb|mld_precset| is used only to set
non-default values of the parameters (see Tables~\ref{tab:p_type}-\ref{tab:p_coarse}).
In both cases, the construction and the application of the preconditioner
are carried out as for the default multi-level preconditioner.
The code fragments shown in in Figures~\ref{fig:ex_3lh}-\ref{fig:ex_3la} are
included in the example program file \verb|mld_dexample_ml.f90| too.
Finally, Figure~\ref{fig:ex_1l} shows the setup of a one-level
additive Schwarz preconditioner, i.e.\ RAS with overlap 2. The corresponding
example program is available in \verb|mld_dexample_| \verb|1lev.f90|.
For all the previous preconditioners, example programs where the sparse matrix and
the right-hand side are generated by discretizing a PDE with Dirichlet
boundary conditions are also available in the directory \verb|examples/pdegen|.
\ \\
\textbf{Remark 3.} Any PSBLAS-based program using the basic preconditioners
implemented in PSBLAS 2.0, i.e.\ the diagonal and block-Jacobi ones,
can use the diagonal and block-Jacobi preconditioners
implemented in MLD2P4 without any change in the code.
The PSBLAS-based program must be only recompiled
and linked to the MLD2P4 library.
\\
\begin{figure}[tbh]
\begin{center}
\begin{minipage}{.90\textwidth}
{\small
\begin{verbatim}
... ...
! set a three-level hybrid Schwarz preconditioner, which uses
! block Jacobi (with ILU(0) on the blocks) as post-smoother,
! a coarsest matrix replicated on the processors, and the
! LU factorization from UMFPACK as coarse-level solver
call mld_precinit(P,'ML',info,nlev=3)
call_mld_precset(P,mld_smoother_type_,'BJAC',info)
call mld_precset(P,mld_coarse_mat_,'REPL',info)
call mld_precset(P,mld_coarse_solve_,'UMF',info)
... ...
\end{verbatim}
}
\end{minipage}
\caption{Setup of a hybrid three-level Schwarz preconditioner.\label{fig:ex_3lh}}
\end{center}
\end{figure}
\begin{figure}[tbh]
\begin{center}
\begin{minipage}{.90\textwidth}
{\small
\begin{verbatim}
... ...
! set a three-level additive Schwarz preconditioner, which uses
! RAS (with overlap 1 and ILU(0) on the blocks) as pre- and
! post-smoother, and 5 block-Jacobi sweeps (with UMFPACK LU
! on the blocks) as distributed coarsest-level solver
call mld_precinit(P,'ML',info,nlev=3)
call mld_precset(P,mld_ml_type_,'ADD',info)
call_mld_precset(P,mld_smoother_pos_,'TWOSIDE',info)
call mld_precset(P,mld_coarse_sweeps_,5,info)
... ...
\end{verbatim}
}
\end{minipage}
\caption{Setup of an additive three-level Schwarz preconditioner.\label{fig:ex_3la}}
\end{center}
\end{figure}
\begin{figure}[tbh]
\begin{center}
\begin{minipage}{.90\textwidth}
{\small
\begin{verbatim}
... ...
! set RAS with overlap 2 and ILU(0) on the local blocks
call mld_precinit(P,'AS',info)
call mld_precset(P,mld_sub_ovr_,2,info)
... ...
\end{verbatim}
}
\end{minipage}
\caption{Setup of a one-level Schwarz preconditioner.\label{fig:ex_1l}}
\end{center}
\end{figure}
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\section{Introduction}\label{sec:intro}
\markboth{\underline{MLD2P4 User's and Reference Guide}}
{\underline{\ref{sec:overview} Introduction}}
The MLD2P4 library provides ....
\subsection{Programming model}
The MLD2P4 librarary is based on the Single Program Multiple Data
(SPMD) programming model: each process participating in the
computation performs the same actions on a chunk of data. Parallelism
is thus data-driven.
Because of this structure, many subroutines coordinate their action
across the various processes, thus providing an implicit
synchronization point, and therefore \emph{must} be
called simultaneously by all processes participating in the
computation.
However there are many cases where no synchronization, and indeed no
communication among processes, is implied.
Throughout this user's guide each subroutine will be clearly indicated
as:
\begin{description}
\item[Synchronous:] must be called simultaneously by all the
processes in the relevant communication context;
\item[Asynchronous:] may be called in a totally independent manner.
\end{description}
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\section{License\label{sec:license}}
\markboth{\textsc{MLD2P4 User's and Reference Guide}}
{\textsc{\ref{sec:license} License}}
The MLD2P4 is freely distributable under the following copyright
terms: {\small
\begin{verbatim}
MLD2P4 version 1.0
MultiLevel Domain Decomposition Parallel Preconditioners Package
based on PSBLAS (Parallel Sparse BLAS version 2.3)
(C) Copyright 2008
Salvatore Filippone University of Rome Tor Vergata
Alfredo Buttari University of Rome Tor Vergata
Pasqua D'Ambra ICAR-CNR, Naples
Daniela di Serafino Second University of Naples
Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions
are met:
1. Redistributions of source code must retain the above copyright
notice, this list of conditions and the following disclaimer.
2. Redistributions in binary form must reproduce the above copyright
notice, this list of conditions, and the following disclaimer in the
documentation and/or other materials provided with the distribution.
3. The name of the MLD2P4 group or the names of its contributors may
not be used to endorse or promote products derived from this
software without specific written permission.
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED
TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE MLD2P4 GROUP OR ITS CONTRIBUTORS
BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
POSSIBILITY OF SUCH DAMAGE.
\end{verbatim}
}

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\section{General Overview\label{sec:overview}}
\markboth{\textsc{MLD2P4 User's and Reference Guide}}
{\textsc{\ref{sec:overview} General Overview}}
The \textsc{Multi-Level Domain Decomposition Parallel Preconditioners Package based on
PSBLAS (MLD2P4}) provides \emph{multi-level Schwarz preconditioners}~\cite{dd2_96},
to be used in the iterative solutions of sparse linear systems:
\begin{equation}
Ax=b,
\label{system1}
\end{equation}
where $A$ is a square, real or complex, sparse matrix with a symmetric sparsity pattern.
%
%\textbf{NOTA: Caso non simmetrico, aggregazione con $(A+A^T)$ fatta!
%Dovremmo implementare uno smoothed prolongator
%adeguato e fare qualcosa di consistente anche con 1-lev Schwarz.}
%
These preconditioners have the following general features:
\begin{itemize}
\item both \emph{additive and hybrid multilevel} variants are implemented,
i.e.\ variants that are additive among the levels and inside each level, and variants
that are multiplicative among the levels and additive inside each level;
the basic Additive Schwarz (AS) preconditioners are obtained by considering only one level;
\item a \emph{purely algebraic} approach is used to
generate a sequence of coarse-level corrections to a basic AS preconditioner, without
explicitly using any information on the geometry of the original problem (e.g.\ the
discretization of a PDE). The \emph{smoothed aggregation} technique is applied
as algebraic coarsening strategy~\cite{BREZINA_VANEK,VANEK_MANDEL_BREZINA}.
\end{itemize}
The package is written in \emph{Fortran~95}, following an
\emph{object-oriented approach} through the exploitation of features
such as abstract data type creation, functional
overloading and dynamic memory management.
% , while providing a smooth
% path towards the integration in legacy application codes.
The parallel implementation is based
on a Single Program Multiple Data (SPMD) paradigm for distributed-memory architectures.
Single and double precision implementations of MLD2P4 are available for both the
real and the complex case, that can be used through a single interface.
MLD2P4 has been designed to implement scalable and easy-to-use multilevel preconditioners
in the context of the \emph{PSBLAS (Parallel Sparse BLAS)
computational framework}~\cite{psblas_00}.
PSBLAS is a library originally developed to address the parallel implementation of
iterative solvers for sparse linear system, by providing basic linear algebra
operators and data management facilities for distributed sparse matrices; it
also includes parallel Krylov solvers, built on the top of the basic PSBLAS kernels.
The preconditioners available in MLD2P4 can be used with these Krylov solvers.
The choice of PSBLAS has been mainly motivated by the need of having
a portable and efficient software infrastructure implementing ``de facto'' standard
parallel sparse linear algebra kernels, to pursue goals such as performance,
portability, modularity ed extensibility in the development of the preconditioner
package. On the other hand, the implementation of MLD2P4 has led to some
revisions and extentions of the PSBLAS kernels, leading to the
recent PSBLAS 2.0 version~\cite{PSBLASGUIDE}. The inter-process comunication required
by MLD2P4 is encapsulated into the PSBLAS routines, except few cases where
MPI~\cite{MPI1} is explicitly called. Therefore, MLD2P4 can be run on any parallel
machine where PSBLAS and MPI implementations are available.
MLD2P4 has a layered and modular software architecture where three main layers can be identified.
The lower layer consists of the PSBLAS kernels, the middle one implements
the construction and application phases of the preconditioners, and the upper one
provides a uniform and easy-to-use interface to all the preconditioners.
This architecture allows for different levels of use of the package:
few black-box routines at the upper layer allow non-expert users to easily
build any preconditioner available in MLD2P4 and to apply it within a PSBLAS Krylov solver.
On the other hand, the routines of the middle and lower layer can be used and extended
by expert users to build new versions of multi-level Schwarz preconditioners.
We provide here a description of the upper-layer routines, but not of the
medium-layer ones.
This guide is organized as follows. General information on the distribution of the source code
is reported in Section~\ref{sec:distribution}, while details on the configuration
and installation of the package are given in Section~\ref{sec:building}. A description of
multi-level Schwarz preconditioners based on smoothed aggregation is provided
in Section~\ref{sec:background}, to help the users in choosing among the different preconditioners
implemented in MLD2P4. The basics for building and applying the preconditioners
with the Krylov solvers implemented in PSBLAS are reported in Section~\ref{sec:started}, where the
Fortran 95 codes of a few sample programs are also shown. A reference guide for
the upper-layer routines of MLD2P4, that are the user interface, is provided
in Section~\ref{sec:userinterface}. The error handling mechanism used by the package is briefly described
in Section~\ref{sec:errors}. The copyright terms concerning the distribution and modification
of MLD2P4 are reported in Appendix~\ref{sec:license}.
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\section{Preconditioner routines}
\label{sec:precs}
\markboth{\underline{MLD2P4 User's and Reference Guide}}
{\underline{\ref{sec:precs} Preconditioners}}
% \section{Preconditioners}
\label{sec:psprecs}
The MLD2P4 library contains the implementation of many preconditioning
techniques. The preconditioners may be applied as normal ``base''
preconditioners; alternatively multiple ``base'' preconditioners may
be combined in a multilevel framework.
The base (one-level) preconditioners include:
\begin{itemize}
\item Diagonal Scaling
\item Block Jacobi
\item Additive Schwarz, Restricted Additive Schwarz and
Additive Schwarz with Harmonic extensions;
\end{itemize}
The Jacobi and Additive Schwarz preconditioners can make use of the
following solvers:
\begin{itemize}
\item Level-$p$ Incomplete LU factorization ($ILU(p)$);
\item Threshold Incomplete LU factorization ($ILU(\tau,p)$);
\item Complete LU factorization by means of the following optional
external packages:
\begin{itemize}
\item UMFPACK;
\item SuperLU;
\item SuperLU\_Dist.
\end{itemize}
\end{itemize}
The supporting data type and subroutine interfaces are defined in the
module \verb|mld_prec_mod|; the module also overrides the variables
and tyep definitions of \verb|psb_prec_mod| so as to function as a
drop-in replacement for the PSBLAS methods. Thus if the user does not
wish to employ the additional MLD2P4 capabitlities, it is possible to
migrate an existing PSBLAS program without any source code
modifications, only a recompilation is needed.
%% We also provide a companion package of multi-level Additive
%% Schwarz preconditioners called MD2P4; this is actually a family of
%% preconditioners since there is the possibility to choose between
%% many variants, and is currently in an experimental stateIts
%% documentation is planned to appear after stabilization of the
%% package, which will characterize release 2.1 of our library.
\subroutine{mld\_precinit}{Initialize a preconditioner}
\syntax{call mld\_precinit}{prec, ptype, info}
\syntax*{call mld\_precinit}{prec, ptype, info, nlev}
\begin{description}
\item[Type:] Asynchronous.
\item[\bf On Entry]
\item[ptype] the type of preconditioner.
Scope: {\bf global} \\
Type: {\bf required}\\
Intent: {\bf in}.\\
Specified as: a character string, see usage notes.
\item[nlev] Number of levels in a multilevel precondtioner.
Scope: {\bf global} \\
Type: {\bf optional}\\
Specified as: an integer value, see usage notes.
%% \item[rs]
%% Scope: {\bf global} \\
%% Type: {\bf optional}\\
%% Specified as: a long precision real number.
\item[\bf On Exit]
\item[prec]
Scope: {\bf local} \\
Type: {\bf required}\\
Intent: {\bf inout}.\\
Specified as: a preconditioner data structure \precdata.
\item[info]
Scope: {\bf global} \\
Type: {\bf required}\\
Intent: {\bf out}.\\
Error code: if no error, 0 is returned.
\end{description}
\subsection*{Usage Notes}
%% The PSBLAS 2.0 contains a number of preconditioners, ranging from a
%% simple diagonal scaling to 2-level domain decomposition. These
%% preconditioners may use the SuperLU or the UMFPACK software, if
%% installed; see~\cite{SUPERLU,UMFPACK}.
Legal inputs to this subroutine are interpreted depending on the
$ptype$ string as follows\footnote{The string is case-insensitive}:
\begin{description}
\item[NONE] No preconditioning, i.e. the preconditioner is just a copy
operator.
\item[DIAG] Diagonal scaling; each entry of the input vector is
multiplied by the reciprocal of the sum of the absolute values of
the coefficients in the corresponding row of matrix $A$;
\item[BJAC] Precondition by a factorization of the
block-diagonal of matrix $A$, where block boundaries are determined
by the data allocation boundaries for each process; requires no
communication.
\item[AS] Additive Schwarz; default is to apply the Restricted
Additive Schwarz variant, with an $ILU(0)$ factorization
\item[ML] Multilevel preconditioner.
\end{description}
\subroutine{mld\_precset}{Set preconditioner features}
\syntax{call mld\_precset}{prec, what, val, info, ilev}
\begin{description}
\item[Type:] Asynchronous.
\item[\bf On Entry]
\item[prec] the preconditioner.\\
Scope: {\bf local} \\
Type: {\bf required}\\
Intent: {\bf inout}.\\
Specified as: an already initialized precondtioner data structure \precdata\\
\item[what] The feature to be set. \\
Scope: {\bf local} \\
Type: {\bf required}\\
Intent: {\bf in}.\\
Specified as: an integer constants. Symbolic names are available in
the library module, see usage notes for legal values.
\item[val] The value to set the chosen feature to. \\
Scope: {\bf local} \\
Type: {\bf required}\\
Intent: {\bf in}.\\
Specified as: an integer, double precision or character variable.
Symbolic names for some choices are available in the library module,
see usage notes for legal values.
\item[ilev] The level of a multilevel preconditioner to which the
feature choice should apply.\\
Scope: {\bf global} \\
Type: {\bf optional}\\
Specified as: an integer value, see usage notes.
\end{description}
\begin{description}
\item[\bf On Return]
\item[prec] the preconditioner.\\
Scope: {\bf local} \\
Type: {\bf required}\\
Intent: {\bf inout}.\\
Specified as: a precondtioner data structure \precdata\\
\item[info] Error code.\\
Scope: {\bf local} \\
Type: {\bf required} \\
Intent: {\bf out}.\\
An integer value; 0 means no error has been detected.
\end{description}
\subsection*{Usage Notes}
Legal inputs to this subroutine are interpreted depending on the value
of \verb|what| input as follows
\begin{description}
\item[mld\_coarse\_mat\_]
\end{description}
\subroutine{mld\_precbld}{Builds a preconditioner}
\syntax{call mld\_precbld}{a, desc\_a, prec, info}
\begin{description}
\item[Type:] Synchronous.
\item[\bf On Entry]
\item[a] the system sparse matrix.
Scope: {\bf local} \\
Type: {\bf required}\\
Intent: {\bf in}, target.\\
Specified as: a sparse matrix data structure \spdata.
\item[prec] the preconditioner.\\
Scope: {\bf local} \\
Type: {\bf required}\\
Intent: {\bf inout}.\\
Specified as: an already initialized precondtioner data structure \precdata\\
\item[desc\_a] the problem communication descriptor.
Scope: {\bf local} \\
Type: {\bf required}\\
Intent: {\bf in}, target.\\
Specified as: a communication descriptor data structure \descdata.
%% \item[upd]
%% Scope: {\bf global} \\
%% Type: {\bf optional}\\
%% Intent: {\bf in}.\\
%% Specified as: a character.
\end{description}
\begin{description}
\item[\bf On Return]
\item[prec] the preconditioner.\\
Scope: {\bf local} \\
Type: {\bf required}\\
Intent: {\bf inout}.\\
Specified as: a precondtioner data structure \precdata\\
\item[info] Error code.\\
Scope: {\bf local} \\
Type: {\bf required} \\
Intent: {\bf out}.\\
An integer value; 0 means no error has been detected.
\end{description}
\subroutine{mld\_precaply}{Preconditioner application routine}
\syntax{call mld\_precaply}{prec,x,y,desc\_a,info,trans,work}
\syntax*{call mld\_precaply}{prec,x,desc\_a,info,trans}
\begin{description}
\item[Type:] Synchronous.
\item[\bf On Entry]
\item[prec] the preconditioner.
Scope: {\bf local} \\
Type: {\bf required}\\
Intent: {\bf in}.\\
Specified as: a preconditioner data structure \precdata.
\item[x] the source vector.
Scope: {\bf local} \\
Type: {\bf required}\\
Intent: {\bf inout}.\\
Specified as: a double precision array.
\item[desc\_a] the problem communication descriptor.
Scope: {\bf local} \\
Type: {\bf required}\\
Intent: {\bf in}.\\
Specified as: a communication data structure \descdata.
\item[trans]
Scope: {\bf } \\
Type: {\bf optional}\\
Intent: {\bf in}.\\
Specified as: a character.
\item[work] an optional work space
Scope: {\bf local} \\
Type: {\bf optional}\\
Intent: {\bf inout}.\\
Specified as: a double precision array.
\end{description}
\begin{description}
\item[\bf On Return]
\item[y] the destination vector.
Scope: {\bf local} \\
Type: {\bf required}\\
Intent: {\bf inout}.\\
Specified as: a double precision array.
\item[info] Error code.\\
Scope: {\bf local} \\
Type: {\bf required} \\
Intent: {\bf out}.\\
An integer value; 0 means no error has been detected.
\end{description}
\subroutine{mld\_prec\_descr}{Prints a description of current preconditioner}
\syntax{call mld\_prec\_descr}{prec}
\begin{description}
\item[Type:] Asynchronous.
\item[\bf On Entry]
\item[prec] the preconditioner.
Scope: {\bf local} \\
Type: {\bf required}\\
Intent: {\bf in}.\\
Specified as: a preconditioner data structure \precdata.
\end{description}
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% Contents: The title page
% $Id: title.tex 1999 2007-10-29 15:25:27Z sfilippo $
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\ifcase\pdfoutput % We're not running pdftex
{\Large\bfseries MLD2P4\\[.8ex] User's and Reference Guide}\\
\emph{\large A guide for the Multi-Level Domain Decomposition \\[.6ex]
Parallel Preconditioners Package
based on PSBLAS}
{\bfseries Pasqua D'Ambra}\\
ICAR-CNR, Naples, Italy\\[3ex]
{\bfseries Daniela di Serafino}\\
Second University of Naples, Italy\\[3ex]
{\bfseries Salvatore Filippone} \\
University of Rome ``Tor Vergata'', Italy
%\\[10ex]
%\today
Software version: 1.0\\
%\today
July 24, 2008
\or
\pdfbookmark{MLD2P4 User's and Reference Guide}{title}
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\noindent\hspace*{\centeroffset}\makebox[0pt][l]{\begin{minipage}{\textwidth}
\flushright
{\Huge\bfseries MLD2P4\\[.8ex] User's and Reference Guide
}
\noindent\rule[-1ex]{\textwidth}{5pt}\\[2.5ex]
\hfill\emph{\Large A guide for the Multi-Level Domain Decomposition \\[.6ex]
Parallel Preconditioners Package
based on PSBLAS}
\end{minipage}}
\vspace{\stretch{1}}
\noindent\hspace*{\centeroffset}\makebox[0pt][l]{\begin{minipage}{\textwidth}
\flushright
{\large\bfseries Pasqua D'Ambra}\\
\large ICAR-CNR, Naples, Italy\\[3ex]
{\large\bfseries Daniela di Serafino}\\
\large Second University of Naples, Italy\\[3ex]
{\large\bfseries Salvatore Filippone} \\
\large University of Rome ``Tor Vergata'', Italy
%\\[10ex]
%\today
\end{minipage}}
\vspace{\stretch{1}}
\noindent\hspace*{\centeroffset}\makebox[0pt][l]{\begin{minipage}{\textwidth}
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\large Software version: 1.0\\
%\today
\large July 24, 2008
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{\Huge\bfseries MLD2P4\\[.8ex] User's and Reference Guide
}
\noindent\rule[-1ex]{\textwidth}{5pt}\\[2.5ex]
\hfill\emph{\Large A guide for the Multi-Level Domain Decomposition \\[.6ex]
Parallel Preconditioners Package
based on PSBLAS}
\end{minipage}}
\vspace{\stretch{1}}
\noindent\hspace*{\centeroffset}\makebox[0pt][l]{\begin{minipage}{\textwidth}
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{\large\bfseries Pasqua D'Ambra}\\
\large ICAR-CNR, Naples, Italy\\[3ex]
{\large\bfseries Daniela di Serafino}\\
\large Second University of Naples, Italy\\[3ex]
{\large\bfseries Salvatore Filippone} \\
\large University of Rome ``Tor Vergata'', Italy
%\\[10ex]
%\today
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\vspace{\stretch{1}}
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\large Software version: 1.0\\
%\today
\large July 24, 2008
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\include{gettingstarted}
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\appendix
\include{license}
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\begin{document}
{\Large\bfseries MLD2P4\\[.8ex] User's and Reference Guide}\\[\baselineskip]
\emph{\large A guide for the Multi-Level Domain Decomposition
Parallel Preconditioners Package
based on PSBLAS}\\[3ex]
{\bfseries Pasqua D'Ambra}\\
ICAR-CNR, Naples, Italy\\
{\bfseries Daniela di Serafino}\\
Second University of Naples, Italy\\
{\bfseries Salvatore Filippone} \\
University of Rome ``Tor Vergata'', Italy\\[2ex]
%\\[10ex]
%\today
Software version: 1.0\\
%\today
July 24, 2008
\clearpage
\ \\
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\include{overview}
\include{distribution}
\include{building}
\include{background}
\include{gettingstarted}
\include{userinterface}
%\include{advanced}
\include{errors}
%\include{listofroutines}
\cleardoublepage
\appendix
\include{license}
\cleardoublepage
\include{bibliography}
\end{document}
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\section{User Interface\label{sec:userinterface}}
\markboth{\textsc{MLD2P4 User's and Reference Guide}}
{\textsc{\ref{sec:userinterface} User Interface}}
The basic user interface of MLD2P4 consists of six routines. The four routines \verb|mld_| \verb|precinit|,
\verb|mld_precset|, \verb|mld_precbld| and \verb|mld_precaply| encapsulate all the functionalities
for the setup and the application of any one-level and multi-level
preconditioner implemented in the package.
The routine \verb|mld_precfree| deallocates the preconditioner data structure, while
\verb|mld_precdescr| prints a description of the preconditioner setup by the user.
For each routine, the same user interface is overloaded with
respect to the real/complex case and the single/double precision;
arguments with appropriate data types must be passed to the routine,
i.e.
\begin{itemize}
\item the sparse matrix data structure, containing the matrix to be
preconditioned, must be of type \verb|mld_|\emph{x}\verb|spmat_type|
with \emph{x} = \verb|s| for real single precision, \emph{x} = \verb|d|
for real double precision, \emph{x} = \verb|c| for complex single precision,
\emph{x} = \verb|z| for complex double precision;
\item the preconditioner data structure must be of type
\verb|mld_|\emph{x}\verb|prec_type|, with \emph{x} =
\verb|s|, \verb|d|, \verb|c|, \verb|z|, according to the sparse
matrix data structure;
\item the arrays containing the vectors $v$ and $w$ involved in
the preconditioner application $w=M^{-1}v$ must be of type
\emph{type}\verb|(|\emph{kind\_parameter}\verb|)|, with \emph{type} =
\verb|real|, \verb|complex| and \emph{kind\_parameter} = \verb|kind(1.e0)|,
\verb|kind(1.d0)|, according to the sparse matrix and preconditioner
data structure; note that the PSBLAS module \verb|psb_base_mod|
provides the constants \verb|psb_spk_|
= \verb|kind(1.e0)| and \verb|psb_dpk_| = \verb|kind(1.d0)|;
\item real parameters defining the preconditioner must be declared
according to the precision of the sparse matrix and preconditioner
data structures (see Section~\ref{sec:precset}).
\end{itemize}
A description of each routine is given in the remainder of this section.
\clearpage
\subsection{Subroutine mld\_precinit\label{sec:precinit}}
\begin{center}
\verb|mld_precinit(p,ptype,info)| \\
\verb|mld_precinit(p,ptype,info,nlev)| \\
\end{center}
\noindent
This routine allocates and initializes the preconditioner data structure,
according to the preconditioner type chosen by the user.
{\vskip2\baselineskip\noindent\large\bfseries Arguments}
\begin{tabular}{p{1.2cm}p{12cm}}
\verb|p| & \verb|type(mld_|\emph{x}\verb|prec_type), intent(inout)|.\\
& The preconditioner data structure. Note that \emph{x}
must be chosen according to the real/complex, single/double
precision version of MLD2P4 under use.\\
\verb|ptype| & \verb|character(len=*), intent(in)|.\\
& The type of preconditioner. Its values are specified
in Table~\ref{tab:precinit}.\\
& Note that the strings are case insensitive.\\
\verb|info| & \verb|integer, intent(out)|.\\
& Error code. If no error, 0 is returned. See Section~\ref{sec:errors} for details.\\
\verb|nlev| & \verb|integer, optional, intent(in)|.\\
& The number of levels of the multilevel preconditioner.
If \verb|nlev| is not present and \verb|ptype|=\verb|'ML'|, \verb|'ml'|,
then \verb|nlev|=2 is assumed. Otherwise, \verb|nlev| is ignored.\\
\end{tabular}
\clearpage
\subsection{Subroutine mld\_precset\label{sec:precset}}
\begin{center}
\verb|mld_precset(p,what,val,info)|\\
\end{center}
\noindent
This routine sets the parameters defining the preconditioner. More
precisely, the parameter identified by \verb|what| is assigned the value
contained in \verb|val|.
{\vskip2\baselineskip\noindent\large\bfseries Arguments}
\begin{tabular}{p{1.2cm}p{12cm}}
\verb|p| & \verb|type(mld_|\emph{x}\verb|prec_type), intent(inout)|.\\
& The preconditioner data structure. Note that \emph{x} must
be chosen according to the real/complex, single/double precision
version of MLD2P4 under use.\\
\verb|what| & \verb|integer, intent(in)|. \\
& The number identifying the parameter to be set.
A mnemonic constant has been associated to each of these
numbers, as reported in Tables~\ref{tab:p_type}-\ref{tab:p_coarse}.\\
\verb|val | & \verb|integer| \emph{or} \verb|character(len=*)| \emph{or}
\verb|real(psb_spk_)| \emph{or} \verb|real(psb_dpk_)|,
\verb|intent(in)|.\\
& The value of the parameter to be set. The list of allowed
values and the corresponding data types is given in
Tables~\ref{tab:p_type}-\ref{tab:p_coarse}.
When the value is of type \verb|character(len=*)|,
it is also treated as case insensitive.\\
\verb|info| & \verb|integer, intent(out)|.\\
& Error code. If no error, 0 is returned. See Section~\ref{sec:errors}
for details.\\
%
%\verb|ilev| & \verb|integer, optional, intent(in)|.\\
% & For the multilevel preconditioner, the level at which the
% preconditioner parameter has to be set.
% The levels are numbered in increasing
% order starting from the finest one, i.e.\ level 1 is the finest level.
% If \verb|ilev| is not present, the parameter identified by \verb|what|
% is set at all the appropriate levels (see Table~\ref{tab:params}).
\end{tabular}
\ \\
A variety of (one-level and multi-level) preconditioners can be obtained
by a suitable setting of the preconditioner parameters. These parameters
can be logically divided into four groups, i.e.\ parameters defining
\begin{enumerate}
\item the type of multi-level preconditioner;
\item the one-level preconditioner used as smoother;
\item the aggregation algorithm;
\item the coarse-space correction at the coarsest level.
\end{enumerate}
A list of the parameters that can be set, along with their allowed and
default values, is given in Tables~\ref{tab:p_type}-\ref{tab:p_coarse}.
For a detailed description of the meaning of the parameters, please
refer to Section~\ref{sec:background}.
%
%Note that the routine allows to set different features of the
%preconditioner at each level through the use of \verb|ilev|.
%This should be done by users with experience in the field of
%multi-level preconditioners. Non-expert users are recommended
%to call \verb| mld_precset| without specifying \verb|ilev|.
\bsideways
\begin{center}
\begin{tabular}{|l|l|p{2cm}|l|p{7cm}|}
\hline
\verb|what| & \textsc{data type} & \verb|val| & \textsc{default} &
\textsc{comments} \\ \hline
%\multicolumn{5}{|c|}{\emph{type of the multi-level preconditioner}}\\ \hline
\verb|mld_ml_type_| & \verb|character(len=*)|
& \texttt{'ADD'} \ \ \ \texttt{'MULT'}
& \texttt{'MULT'}
& Basic multi-level framework: additive or multiplicative
among the levels (always additive inside a level). \\ \hline
\verb|mld_smoother_type_|& \verb|character(len=*)|
& \texttt{'DIAG'} \ \ \ \texttt{'BJAC'} \ \ \ \texttt{'AS'}
& \texttt{'AS'}
& Basic one-level preconditioner (i.e.\ smoother): diagonal,
block Jacobi, AS. \\ \hline
\verb|mld_smoother_pos_| & \verb|character(len=*)|
& \texttt{'PRE'} \ \ \ \texttt{'POST'} \ \ \ \texttt{'TWOSIDE'}
& \texttt{'POST'}
& ``Position'' of the smoother: pre-smoother, post-smoother,
pre- and post-smoother. \\
\hline
\end{tabular}
\end{center}
\caption{Parameters defining the type of multi-level preconditioner.
\label{tab:p_type}}
\esideways
\bsideways
\begin{center}
\begin{tabular}{|l|l|p{3.2cm}|l|p{7cm}|}
\hline
\verb|what| & \textsc{data type} & \verb|val| & \textsc{default} &
\textsc{comments} \\ \hline
%\multicolumn{5}{|c|}{\emph{basic one-level preconditioner (smoother)}} \\ \hline
\verb|mld_sub_ovr_| & \verb|integer|
& any~int.~num.~$\ge 0$
& 1
& Number of overlap layers. \\ \hline
\verb|mld_sub_restr_| & \verb|character(len=*)|
& \texttt{'HALO'} \hspace{2.5cm} \texttt{'NONE'}
& \texttt{'HALO'}
& Type of restriction operator:
\texttt{'HALO'} for taking into account the overlap, \texttt{'NONE'}
for neglecting it. \\ \hline
\verb|mld_sub_prol_| & \verb|character(len=*)|
& \texttt{'SUM'} \hspace{2.5cm} \texttt{'NONE'}
& \texttt{'NONE'}
& Type of prolongation operator:
\texttt{'SUM'} for adding the contributions from the overlap, \texttt{'NONE'}
for neglecting them. \\ \hline
\verb|mld_sub_solve_| & \verb|character(len=*)|
& \texttt{'ILU'} \hspace{2.5cm} \texttt{'MILU'} \hspace{2.5cm} \texttt{'ILUT'}
\hspace{2.5cm} \texttt{'UMF'} \hspace{2.5cm} \texttt{'SLU'}
& \texttt{'UMF'}
& Local solver: ILU($p$), MILU($p$), ILU($p,t$), LU from UMFPACK, LU from SuperLU
(plus triangular solve). \\ \hline
\verb|mld_sub_fillin_| & \verb|integer|
& Any~int.~num.~$\ge 0$
& 0
& Fill-in level $p$ of the incomplete LU factorizations. \\ \hline
\verb|mld_sub_iluthrs_| & \verb|real(|\emph{kind\_parameter}\verb|)|
& Any~real~num.~$\ge 0$
& 0
& Drop tolerance $t$ in the ILU($p,t$) factorization. \\ \hline
\verb|mld_sub_ren_| & \verb|character(len=*)|
& \texttt{'RENUM\_NONE'} \texttt{'RENUM\_GLOBAL'} %, \texttt{'RENUM_GPS'}
& \texttt{'RENUM\_NONE'}
& Row and column reordering of the local submatrices: no reordering,
reordering according to the global numbering of the rows and columns of
the whole matrix. \\
\hline
\end{tabular}
\end{center}
\caption{Parameters defining the one-level preconditioner used as smoother.
\label{tab:p_smoother}}
\esideways
\bsideways
\begin{center}
\begin{tabular}{|l|l|p{2.3cm}|p{2.6cm}|p{7cm}|}
\hline
\verb|what| & \textsc{data type} & \verb|val| & \textsc{default} &
\textsc{comments} \\ \hline
%\multicolumn{5}{|c|}{\emph{aggregation algorithm}} \\ \hline
\verb|mld_aggr_alg_| & \verb|character(len=*)|
& \texttt{'DEC'}
& \texttt{'DEC'}
& Aggregation algorithm. Currently, only the decoupled aggregation is available. \\ \hline
\verb|mld_aggr_kind_| & \verb|character(len=*)|
& \texttt{'SMOOTH'} \hspace{2.5cm} \texttt{'RAW'}
& \texttt{'SMOOTH'}
& Type of aggregation: smoothed, raw (i.e.\ using the tentative prolongator). \\ \hline
\verb|mld_aggr_thresh_| & \verb|real(|\emph{kind\_parameter}\verb|)|
& Any~real~num. $\in [0, 1]$
& 0
& Threshold $\theta$ in the aggregation algorithm. \\ \hline
\verb|mld_aggr_eig_| & \verb|character(len=*)|
& \texttt{'A\_NORMI'}
& \texttt{'A\_NORMI'}
& Estimate of the eigenvalue $D^{-1}A$ with largest modulus,
to build the damping parameter $\omega$ in the smoothed aggregation.
Currently, only the infinity norm of
the matrix is available. \\ \hline
\verb|mld_aggr_damp_| & \verb|real(|\emph{kind\_parameter}\verb|)|
& Any~real~num.
& $4/(3||D^{-1}A||_\infty)$
& Damping parameter $\omega$ in the smoothed aggregation algorithm.
If the user specifies a negative value, then $\omega$
is set to its default value;
otherwise, $\omega$ is set to the value provided by the
user. In the latter case no estimate of the eigenvalue of
$D^{-1}A$ with largest modulus is computed.\\
\hline
\end{tabular}
\end{center}
\caption{Parameters defining the aggregation algorithm.
\label{tab:p_aggregation}}
\esideways
\bsideways
\begin{center}
\begin{tabular}{|l|l|p{3.2cm}|l|p{7cm}|}
\hline
\verb|what| & \textsc{data type} & \verb|val| & \textsc{default} &
\textsc{comments} \\ \hline
%\multicolumn{5}{|c|}{\emph{coarse-space correction at the coarsest level}}\\ \hline
\verb|mld_coarse_mat_| & \verb|character(len=*)|
& \texttt{'DISTR'} \hspace{2.5cm} \texttt{'REPL'}
& \texttt{'DISTR'}
& Coarsest matrix: distributed among the processors or
replicated on each of them. \\ \hline
\verb|mld_coarse_solve_| & \verb|character(len=*)|
& \texttt{'BJAC'} \hspace{2.5cm} \texttt{'UMF'} \hspace{2.5cm}
\texttt{'SLU'} \hspace{2.5cm} \texttt{'SLUDIST'}
& \texttt{'BJAC'}
& Solver used at the coarsest level: block Jacobi, sequential
LU from UMFPACK, sequential LU from SuperLU,
distributed LU from SuperLU\_Dist.
\texttt{'BJAC'} and \texttt{'SLUDIST'} require the coarsest
matrix to be distributed, while \texttt{'UMF'} and
\texttt{'SLU'} require it to be replicated. \\ \hline
\verb|mld_coarse_subsolve_| & \verb|character(len=*)|
& \texttt{'ILU'} \hspace{2.5cm} \texttt{'MILU'}
\hspace{2.5cm} \texttt{'ILUT'}
\hspace{2.5cm} \texttt{'UMF'} \hspace{2.5cm} \texttt{'SLU'}
& \texttt{'UMF'}
& Solver for the diagonal blocks of the coarse matrix,
in case the block Jacobi solver
is chosen as coarsest-level solver: ILU($p$), MILU($p$),
ILU($p,t$), LU from UMFPACK,
LU from SuperLU, plus triangular solve. \\ \hline
\verb|mld_coarse_sweeps_|& \verb|integer|
& Any~int.~num.~$> 0$
& 4
& Number of Block-Jacobi sweeps when 'BJAC' is used as
coarsest-level solver. \\ \hline
\verb|mld_coarse_fillin_| & \verb|integer|
& Any~int.~num.~$\ge 0$
& 0
& Fill-in level $p$ of the incomplete LU factorizations. \\ \hline
\verb|mld_coarse_iluthrs_| & \verb|real(|\emph{kind\_parameter}\verb|)|
& Any~real.~num.~$\ge 0$
& 0
& Drop tolerance $t$ in the ILU($p,t$) factorization. \\
\hline
\end{tabular}
\end{center}
\caption{Parameters defining the coarse-space correction at the coarsest
level.\label{tab:p_coarse}}
\esideways
\clearpage
\subsection{Subroutine mld\_precbld\label{sec:precbld}}
\begin{center}
\verb|mld_precbld(a,desc_a,p,info)|\\
\end{center}
\noindent
This routine builds the preconditioner according to the requirements made by
the user through the routines \verb|mld_precinit| and \verb|mld_precset|.
{\vskip2\baselineskip\noindent\large\bfseries Arguments}
\begin{tabular}{p{1.2cm}p{12cm}}
\verb|a| & \verb|type(psb_|\emph{x}\verb|spmat_type), intent(in)|. \\
& The sparse matrix structure containing the local part of the
matrix to be preconditioned. Note that \emph{x} must be chosen according
to the real/complex,
single/double precision version of MLD2P4 under use.
See the PSBLAS User's Guide for details \cite{PSBLASGUIDE}.\\
\verb|desc_a| & \verb|type(psb_desc_type), intent(in)|. \\
& The communication descriptor of \verb|a|. See the PSBLAS User's Guide for
details \cite{PSBLASGUIDE}.\\
\verb|p| & \verb|type(mld_|\emph{x}\verb|prec_type), intent(inout)|.\\
& The preconditioner data structure. Note that \emph{x} must be chosen according
to the real/complex, single/double precision version of MLD2P4 under use.\\
\verb|info| & \verb|integer, intent(out)|.\\
& Error code. If no error, 0 is returned. See Section~\ref{sec:errors} for details.\\
\end{tabular}
\clearpage
\subsection{Subroutine mld\_precaply\label{sec:precaply}}
\begin{center}
\verb|mld_precaply(p,x,y,desc_a,info)|\\
\verb|mld_precaply(p,x,y,desc_a,info,trans,work)|\\
\end{center}
\noindent
This routine computes $y = op(M^{-1})\, x$, where $M$ is a previously built
preconditioner, stored into \verb|p|, and $op$
denotes the preconditioner itself or its transpose, according to
the value of \verb|trans|.
Note that, when MLD2P4 is used with a Krylov solver from PSBLAS,
\verb|mld_precaply| is called within the PSBLAS routine \verb|mld_krylov|
and hence it is completely transparent to the user.
{\vskip2\baselineskip\noindent\large\bfseries Arguments}
\begin{tabular}{p{1.2cm}p{12cm}}
\verb|p| & \verb|type(mld_|\emph{x}\verb|prec_type), intent(inout)|.\\
& The preconditioner data structure, containing the local part of $M$.
Note that \emph{x} must be chosen according
to the real/complex, single/double precision version of MLD2P4 under use.\\
\verb|x| & \emph{type}\verb|(|\emph{kind\_parameter}\verb|), dimension(:), intent(in)|.\\
& The local part of the vector $x$. Note that \emph{type} and
\emph{kind\_parameter} must be chosen according
to the real/complex, single/double precision version of MLD2P4 under use.\\
\verb|y| & \emph{type}\verb|(|\emph{kind\_parameter}\verb|), dimension(:), intent(out)|.\\
& The local part of the vector $y$. Note that \emph{type} and
\emph{kind\_parameter} must be chosen according
to the real/complex, single/double precision version of MLD2P4 under use.\\
\verb|desc_a| & \verb|type(psb_desc_type), intent(in)|. \\
& The communication descriptor associated to the matrix to be
preconditioned.\\
\verb|info| & \verb|integer, intent(out)|.\\
& Error code. If no error, 0 is returned. See Section~\ref{sec:errors} for details.\\
\verb|trans| & \verb|character(len=1), optional, intent(in).|\\
& If \verb|trans| = \verb|'N','n'| then $op(M^{-1}) = M^{-1}$;
if \verb|trans| = \verb|'T','t'| then $op(M^{-1}) = M^{-T}$
(transpose of $M^{-1})$; if \verb|trans| = \verb|'C','c'| then $op(M^{-1}) = M^{-C}$
(conjugate transpose of $M^{-1})$.\\
\verb|work| & \emph{type}\verb|(|\emph{kind\_parameter}\verb|), dimension(:), optional, target|.\\
& Workspace. Its size should be at
least \verb|4 * psb_cd_get_local_| \verb|cols(desc_a)| (see the PSBLAS User's Guide).
Note that \emph{type} and \emph{kind\_parameter} must be chosen according
to the real/complex, single/double precision version of MLD2P4 under use.\\
\end{tabular}
\clearpage
\subsection{Subroutine mld\_precfree\label{sec:precfree}}
\begin{center}
\verb|mld_precfree(p,info)|\\
\end{center}
\noindent
This routine deallocates the preconditioner data structure.
{\vskip2\baselineskip\noindent\large\bfseries Arguments}
\begin{tabular}{p{1.2cm}p{10.5cm}}
\verb|p| & \verb|type(mld_|\emph{x}\verb|prec_type), intent(inout)|.\\
& The preconditioner data structure. Note that \emph{x} must be chosen according
to the real/complex, single/double precision version of MLD2P4 under use.\\
\verb|info| & \verb|integer, intent(out)|.\\
& Error code. If no error, 0 is returned. See Section~\ref{sec:errors} for details.\\
\end{tabular}
\clearpage
\subsection{Subroutine mld\_precdescr\label{sec:precdescr}}
\begin{center}
\verb|mld_precdescr(p,info)|\\
\verb|mld_precdescr(p,info,iout)|\\
\end{center}
\noindent
This routine prints a description of the preconditioner to the standard output or
to a file. It must be called after \verb|mld_precbld| has been called.
{\vskip2\baselineskip\noindent\large\bfseries Arguments}
\begin{tabular}{p{1.2cm}p{12cm}}
\verb|p| & \verb|type(mld_|\emph{x}\verb|prec_type), intent(in)|.\\
& The preconditioner data structure. Note that \emph{x} must be chosen according
to the real/complex, single/double precision version of MLD2P4 under use.\\
\verb|info| & \verb|integer, intent(out)|.\\
& Error code. If no error, 0 is returned. See Section~\ref{sec:errors} for details.\\
\verb|iout| & \verb|integer, intent(in), optional|.\\
& The id of the file where the preconditioner description
will be printed; the default is the standard output.\\
\end{tabular}
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