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Modified interface to GEALL, GEINS et al.

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Changed ITRACE behaviour in solver routines.
Updated documentation.
This commit is contained in:
Salvatore Filippone
2006-04-24 12:02:04 +00:00
parent 41d7626b66
commit d003f55d95
48 changed files with 18464 additions and 17667 deletions
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docs/pdf/datastruct.tex
+93 -9
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@@ -21,7 +21,7 @@ Fields contained in Sparse matrix structures are:
\item[{\bf aspk}] Contains values of the local distributed sparse
matrix.\\
Specified as: a pointer to an array of rank one of type corresponding
to matrix entries type .
to matrix entries type.
\item[{\bf ia1}] Holds integer information on distributed sparse
matrix. Actual information will depend on data format used.\\
Specified as: a pointer to an integer array of rank one.
@@ -30,7 +30,7 @@ matrix. Actual information will depend on data format used.\\
Specified as: a pointer to an integer array of rank one.
\item[{\bf infoa}] On entry can hold auxiliary information on distributed sparse
matrix. Actual information will depend on data format used.\\
Specified as: integer array of length 10.
Specified as: integer array of length \verb|psb_ifasize_|.
\item[{\bf fida}] Defines the format of the distributed sparse matrix.\\
Specified as: a string of length 5
\item[{\bf descra}] Describe the characteristic of the distributed sparse matrix.\\
@@ -61,7 +61,7 @@ type psb_dspmat_type
integer :: m, k
character :: fida(5)
character :: descra(10)
integer :: infoa(10)
integer :: infoa(psb_ifa_size_)
real(kind(1.d0)), pointer :: aspk(:)
integer, pointer :: ia1(:), ia2(:), pr(:), pl(:)
end type psb_dspmat_type
@@ -72,7 +72,9 @@ end type psb_dspmat_type
\begin{center}
\fbox{\TheSbox}
\end{center}
\caption{\label{fig:spmattype}The PSBLAS defined data type that contains a sparse matrix.}
\caption{\label{fig:spmattype}
The PSBLAS defined data type that
contains a sparse matrix.}
\end{figure}
The following two cases are among the most commonly used:
@@ -98,6 +100,36 @@ column index are stored into \verb|apsk(j)|, \verb|ia1(j)| and
\verb|ia2(j)| respectively.
\end{enumerate}
\end{description}
A sparse matrix has an associated state, which can take the following
values:
\begin{description}
\item[Build:] State entered after the first allocation, and before the
first assembly; in this state it is possible to add nonzero entries.
\item[Assembled:] State entered after the assembly; computations using
the sparse matrix, such as matrix-vector products, are only possible
in this state;
\item[Update:] State entered after a reinitalization; this is used to
handle applications in which the same sparsity pattern is used
multiple times with different coefficients. In this state it is only
possible to enter coefficients for already existing nonzero entries.
\end{description}
\subsubsection{Named Constants}
\label{sec:sp_constants}
\begin{description}
\item[psb\_nztotreq\_] Request to fetch the total number of nonzeroes
stored in a sparse matrix
\item[psb\_nzrowreq\_] Request to fetch the number of nonzeroes in a
given row in a sparse matrix
\item[psb\_dupl\_ovwrt\_] Duplicate coefficients should be overwritten
(i.e. ignore duplications)
\item[psb\_dupl\_add\_] Duplicate coefficients should be added;
\item[psb\_dupl\_err\_] Duplicate coefficients should trigger an error conditino
\item[psb\_upd\_dflt\_] Default update strategy for matrix coefficients;
\item[psb\_upd\_srch\_] Update strategy based on search into the data structure;
\item[psb\_upd\_perm\_] Update strategy based on additional
permutation data (see tools routine description).
\end{description}
\subsection{Descriptor data structure}
@@ -196,6 +228,41 @@ end type psb_desc_type
contains the communication descriptor.}
\end{figure}
A communication descriptor associated with a sparse matrix has a
state, which can take the following values:
\begin{description}
\item[Build:] State entered after the first allocation, and before the
first assembly; in this state it is possible to add communication
requirements among different processes.
\item[Assembled:] State entered after the assembly; computations using
the associated sparse matrix, such as matrix-vector products, are
only possible in this state.
\end{description}
\subsubsection{Named Constants}
\label{sec:cd_constants}
\begin{description}
\item[psb\_none\_] Generic no-op;
\item[psb\_nohalo\_] Do not fetch halo elements;
\item[psb\_halo\_] Fetch halo elements from neighbouring processes;
\item[psb\_sum\_] Sum overlapped elements
\item[psb\_avg\_] Average overlapped elements
%% \item[psb\_square\_root\_] Update with the square root of the average
%% of overlapped elements;
\item[psb\_dec\_type\_] Entry holding decomposition type (in \verb|desc_a%matrix_data|)
\item[psb\_m\_] Entry holding total number of rows
\item[psb\_n\_] Entry holding total number of columns
\item[ psb\_n\_row\_] Entry holding the number of rows stored in the
current process
\item[psb\_n\_col\_] Entry holding the number of columns stored in the
current process
\item[psb\_ctxt\_] Entry holding a copy of the BLACS communication context
\item[psb\_desc\_asb\_] State of the descriptor: assembled,
i.e. suitable for computational tasks.
\item[psb\_desc\_bld\_] State of the descriptor: build, must be
assembled before computational use.
\end{description}
\subsection{Preconditioner data structure}
\label{sec:prec}
PSBLAS-2.0 offers the possibility to use many different types of
@@ -203,8 +270,9 @@ preconditioning schemes. Besides the simple well known preconditioners
like Diagonal Scaling or Block Jacobi (with ILU(0) incomplete
factorization) also more complex preconditioning methods are
implemented like the Additive Schwarz and Two-Level ones. A
preconditioner is held in the \hypertarget{precdata}{{\tt psb\_prec\_type}} data structure
which depends on the \verb|psb_base_prec| reported in
preconditioner is held in the \hypertarget{precdata}{{\tt
psb\_prec\_type}} data structure which depends on the
\verb|psb_base_prec| reported in
figure~\ref{fig:prectype}. The \verb|psb_base_prec|
data type may contain a simple preconditioning matrix with the
associated communication descriptor which may be different than the
@@ -216,7 +284,7 @@ The user can choose the type of preconditioner to be used by means of
the \verb|psb_precset| subroutine; once the type of preconditioning
method is specified, along with all the parameters that characterize
it, the preconditioner data structure can be built using the
\verb|psb_precbuild| subroutine.
\verb|psb_precbld| subroutine.
This data structure wants to be flexible enough to easily allow the
implementation of new kind of preconditioners. The values contained in
the \verb|iprcparm| and \verb|dprcparm| define tha type of
@@ -257,8 +325,24 @@ to be interpreted.
\end{center}
\caption{\label{fig:prectype}The PSBLAS defined data type that contains a preconditioner.}
\end{figure}
\subsubsection{Named Constants}
\label{sec:prec_constants}
\begin{description}
\item[f\_ilu\_n\_] Incomplete LU factorization with $n$ levels of
fill-in; currently only $n=0$ is implemented;
\item[f\_slu\_] Sparse factorization using SuperLU;
\item[f\_umf\_] Sparse factorization using UMFPACK;
\item[add\_ml\_prec\_] Additive multilevel correction;
\item[mult\_ml\_prec\_] Multiplicative multilevel correction;
\item[pre\_smooth\_] Pre-smoothing in applying multiplicative
multilevel corrections;
\item[post\_smooth\_] Post-smoothing in applying multiplicative
multilevel corrections;
\item[smooth\_both\_] Two-sided (i.e. symmetric) smoothing in applying multiplicative
multilevel corrections;
\item[mat\_distr\_] Coarse matrix distributed among processes
\item[mat\_repl\_] Coarse matrix replicated among processes
\end{description}
%%% Local Variables: