*** empty log message ***

docs/pdf/datastruct.tex

@@ -54,18 +54,29 @@ Specified as: integer variable.
 \end{description}
 Values assumed by this fields are compatible with ref. 1 (see \S~\ref{chap:appendix}).\\
 FORTRAN95 interface for distributed sparse matrices containing double precision
-real entries is defined as follows:
+real entries is defined as in figure~\ref{fig:spmattype}.
+\begin{figure}[h!]
+  \begin{Sbox}
+    \begin{minipage}[tl]{0.85\textwidth}
 \begin{verbatim}
-type d_spmat
+type psb_dspmat_type
-     integer     :: m, k
+   integer     :: m, k
-     character*5 :: fida
+   character   :: fida(5)
-     character*1 :: descra(9)
+   character   :: descra(10)
-     integer     :: infoa(10)
+   integer     :: infoa(10)
-     real(kind(1.d0)), pointer :: aspk(:)
+   real(kind(1.d0)), pointer :: aspk(:)
-     integer, pointer :: ia1(:), ia2(:)
+   integer, pointer :: ia1(:), ia2(:), pr(:), pl(:)
-     integer, pointer :: pl(:), pr(:)
+end type psb_dspmat_type
-end type d_spmat	
 \end{verbatim}
+    \end{minipage}
+  \end{Sbox}
+  \setlength{\fboxsep}{8pt}
+  \begin{center}
+    \fbox{\TheSbox}
+  \end{center}
+  \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: 
 \begin{description}
 \item[fida=``CSR''] Compressed storage by rows. In this case the
@@ -91,8 +102,6 @@ column index are stored into \verb|apsk(j)|, \verb|ia1(j)| and
 \end{description}
 
 
-\subsubsection{Sparse Matrix storage formats}
-
 \subsection{Descriptor data structure}
 \label{sec:desc}
 All the general matrix informations and elements to be
@@ -102,7 +111,7 @@ Every structure of this type is associated to a sparse matrix, it
 contains data about general matrix informations and elements to be
 exchanged among processes.  \\ 
 It is not necessary for the user to
-know the internal structure of $psb_desc_type$, it is set in
+know the internal structure of \verb|psb_desc_type|, it is set in
 fully-transparent mode by PSBLAS-TOOLS routines when inserting a new
 sparse matrix, however the definition of the descriptor is the
 following.  
@@ -167,26 +176,89 @@ process then element $i$ contains local index correpondent to global variable $i
 else element $i$ contains -1 (NULL) value.\\
 Specified as: a pointer to an integer array of rank one.
 \end{description}
-FORTRAN90 interface for $decomp\_data$ structures is therefore defined
+FORTRAN95 interface for \verb|psb_desc_type| structures is therefore defined
 as follows:
+\begin{figure}[h!]
+  \begin{Sbox}
+    \begin{minipage}[tl]{0.9\textwidth}
 \begin{verbatim} 
- type decomp_data_type
+type psb_desc_type 
-     integer, pointer :: matrix_data(:)
+   integer, pointer :: matrix_data(:), halo_index(:)
-     integer, pointer :: halo_index(:)
+   integer, pointer :: overlap_elem(:), overlap_index(:)
-     integer, pointer :: ovrlap_elem(:)
+   integer, pointer :: loc_to_glob(:), glob_to_loc(:)
-     integer, pointer :: ovrlap_index(:)
+end type psb_desc_type 
-     integer, pointer :: loc_to_glob(:)
-     integer, pointer :: glob_to_loc (:)
-  end type decomp_data_type
 \end{verbatim}
+    \end{minipage}
+  \end{Sbox}
+  \setlength{\fboxsep}{8pt}
+  \begin{center}
+    \fbox{\TheSbox}
+  \end{center}
+  \caption{\label{fig:desctype}The PSBLAS defined data type that
+    contains the communication descriptor.}
+\end{figure}
 
 \subsection{Preconditioner data structure}
 \label{sec:prec}
-\hypertarget{precdata}{}
+PSBLAS-2.0 offers the possibility to use many different types of
+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
+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
+system communication descriptor in the case of parallel
+preconditioners like the Additive Schwarz one. Then the
+\verb|psb_prec_type| may contain more than one preconditioning matrix
+like in the case of Two-Level (in general Multi-Level) preconditioners.
+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.
+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
+preconditioner along with all the parameters related to it; thus,
+\verb|iprcparm| and \verb|dprcparm| define how the other records have
+to be interpreted.
+\begin{figure}[h!]
+  \small
+  \begin{Sbox}
+    \begin{minipage}[tl]{0.9\textwidth}
+\begin{verbatim}
+  type psb_base_prec
 
+    type(psb_spmat_type), pointer  :: av(:) => null()
+    real(kind(1.d0)), pointer      :: d(:)  => null()
+    type(psb_desc_type), pointer   :: desc_data => null()
+    integer, pointer               :: iprcparm(:) => null()
+    real(kind(1.d0)), pointer      :: dprcparm(:) => null()
+    integer, pointer               :: perm(:)  => null()
+    integer, pointer               :: mlia(:)  => null()
+    integer, pointer               :: invperm(:) => null()
+    integer, pointer               :: nlaggr(:)  => null()
+    type(psb_spmat_type), pointer  :: aorig    => null()
+    real(kind(1.d0)), pointer      :: dorig(:) => null()
     
+ end type psb_base_prec
   
-\subsection{Building and assembling data structures}
+  type psb_prec_type
+    type(psb_base_prec), pointer  :: baseprecv(:) => null()
+    integer                       :: prec, base_prec
+ end type psb_prec_type
+\end{verbatim}
+    \end{minipage}
+  \end{Sbox}
+  \setlength{\fboxsep}{8pt}
+  \begin{center}
+    \fbox{\TheSbox}
+  \end{center}
+  \caption{\label{fig:prectype}The PSBLAS defined data type that contains a preconditioner.}
+\end{figure}
 
 
 

docs/pdf/intro.tex

@@ -1,5 +1,131 @@
 \section{Introduction}
 
+The PSBLAS library, developed with the aim to facilitate the
+parallelization of computationally intensive scientific applications,
+is designed to address parallel implementation of iterative solvers
+for sparse linear systems through the distributed memory paradigm.  It
+includes routines for multiplying sparse matrices by dense matrices,
+solving block diagonal systems with triangular diagonal entries,
+preprocessing sparse matrices, and contains additional routines for
+dense matrix operations.  The current implementation of PSBLAS
+addresses a distributed memory execution model operating with message
+passing. However, the overall design does not preclude different
+implementation paradigms, such as those based on a shared memory
+model.
+
+The PSBLAS library is internally implemented in a mixture of
+Fortran~77 and Fortran~95~\cite{metcalf} programming languages. A
+similar approach has been advocated by a number of authors,
+e.g.~\cite{machiels}.  Moreover, the Fortran~95 facilities for dynamic
+memory management and interface overloading greatly enhance the usability of the PSBLAS
+subroutines. In this way, the library can take care of runtime memory
+requirements that are quite difficult or even impossible to predict at
+implementation or compilation time.  The following presentation of the
+PSBLAS library follows the general structure of the proposal for
+serial Sparse BLAS~\cite{sblas97}, which in its turn is based on the
+proposal for BLAS on dense matrices~\cite{BLAS1,BLAS2,BLAS3}.
+
+The applicability of sparse iterative solvers to many different areas
+causes some terminology problems because the same concept may be
+denoted through different names depending on the application area. The
+PSBLAS features presented in this section will be discussed mainly in terms of finite
+difference discretizations of Partial Differential Equations (PDEs).
+However, the scope of the library is wider than that: for example, it
+can be applied to finite element discretizations of PDEs, and even to
+different classes of problems such as nonlinear optimization, for
+example in optimal control problems.
+
+The design of a solver for sparse linear systems is driven by many
+conflicting objectives, such as limiting occupation of storage
+resources, exploiting regularities in the input data, exploiting
+hardware characteristics of the parallel platform.  To achieve an
+optimal communication to computation ratio on distributed memory
+machines it is essential to keep the {\em data locality} as high as
+possible; this can be done through an appropriate data allocation
+strategy.  The choice of the preconditioner is another very important
+factor that affects efficiency of the implemented application. Optimal
+data distribution requirements for a given preconditioner may conflict
+with distribution requirements of the rest of the solver. Finding the
+optimal trade-off may be very difficult because it is application
+dependent.  Possible solution to these problems and other important
+inputs to the development of the PSBLAS software package has come from
+an established experience in applying the PSBLAS solvers to
+computational fluid dynamics applications.
+
+\section{General overview}
+\label{sec:overview} 
+The PSBLAS library is designed to handle the implementation of
+iterative solvers for sparse linear systems on distributed memory
+parallel computers.  The system coefficient matrix $A$ must be square;
+it may be real or complex, nonsymmetric, and its sparsity pattern
+needs not to be symmetric.  The serial computation parts are based on
+the serial sparse BLAS, so that any extension made to the data
+structures of the serial kernels is available to the parallel
+version. The overall design and parallelization strategy have been
+influenced by the structure of the ScaLAPACK parallel
+library~\cite{scalapack}.  The layered structure of the PSBLAS library
+is shown in figure~\ref{fig:psblas} ; lower layers of the library
+indicate an encapsulation relationship with upper layers. The ongoing
+discussion focuses on the Fortran~95 layer immediately below the
+application layer; two examples of iterative solvers built through the
+PSBLAS routines, will be also given in Section~\ref{sec:itmethd}.  The
+serial parts of the computation on each process are executed through
+calls to the serial sparse BLAS subroutines. In a similar way, the
+inter-process message exchanges are implemented through the Basic
+Linear Algebra Communication Subroutines (BLACS) library~\cite{BLACS}
+that guarantees a portable and efficient communication layer. The
+Message Passing Interface code is encapsulated within the BLACS
+layer. However, in some cases, MPI routines are directly used either
+to improve efficiency or to implement communication patterns for which
+the BLACS package doesn't provide any method.
+
+\begin{figure}[h] \begin{center}
+\includegraphics[scale=0.45]{figures/psblas}
+\end{center}
+\caption{PSBLAS library components hierarchy.\label{fig:psblas}}
+\end{figure}
+
+The PSBLAS library consists of two classes of subroutines that is, the
+{\em computational routines} and the {\em auxiliary routines}.  The
+computational routine set includes:
+
+\begin{itemize}
+\item Sparse matrix by dense matrix product; \item Sparse triangular
+systems solution for block diagonal matrices;
+\item Vector and matrix norms;
+\item Dense matrix sums;
+\item Dot products.
+\end{itemize} 
+The auxiliary routine set includes:
+\begin{itemize}
+\item Communication descriptors allocation;
+\item Dense and sparse matrix allocation;
+\item Dense and sparse matrix build and update;
+\item Sparse matrix and data distribution preprocessing.
+\end{itemize} 
+
+The following naming scheme has been adopted for all the symbols
+internally defined in the PSBLAS software package:
+\begin{itemize}
+\item all the symbols (i.e. subroutine names, data types...) are
+  prefixed by \verb|psb_| 
+\item all the data type names are suffixed by \verb|_type|
+\item all the constant values are suffixed by \verb|_|
+\item all the subroutine names follow the rule \verb|psb_xxname| where
+  \verb|xx| can be either:
+  \begin{itemize}
+  \item \verb|ds|: the routine is related to dense data, 
+  \item \verb|sp|: the routine is related to sparse data, 
+  \item \verb|cd|: the routine is related to communication descriptor (see~\ref{sec:datastruct}).
+  \end{itemize}
+  For example the \verb|psb_dsins|, \verb|psb_spins| and
+  \verb|psb_cdins| perform the same action (see~\ref{sec:toolsrout}) on
+  dense matrices, sparse matrices and communication descriptors
+  respectively.
+  Interface overloading allows the usage of the same subroutine
+  interfaces for both real and complex data.
+\end{itemize}
+
 %%% Local Variables: 
 %%% mode: latex
 %%% TeX-master: "userguide"

docs/pdf/psbrout.tex

@@ -5,7 +5,7 @@
 %      DENSE MATRIX SUM
 %
 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-\subroutine{psb\_axpby}{General Dense Matrix Sum}
+\subroutine{psb\_geaxpby}{General Dense Matrix Sum}
 
 This subroutine is an interface to the computational kernel for
 dense matrix sum:
@@ -16,8 +16,8 @@ where:
 \item[$y$] represents the global dense submatrix $y_{:, jy:jy+n-1}$
 \end{description}
 
-\syntax{call psb\_axpby}{alpha, x, beta, y, desc\_a, info}
+\syntax{call psb\_geaxpby}{alpha, x, beta, y, desc\_a, info}
-\syntax*{call psb\_axpby}{alpha, x, beta, y, desc\_a, info, n, jx, jy}
+\syntax*{call psb\_geaxpby}{alpha, x, beta, y, desc\_a, info, n, jx, jy}
 
 %( calculating y <- alpha*x+beta*y )
 \begin{table}[h]
@@ -103,7 +103,7 @@ An integer value that contains an error code.
 %
 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 
-\subroutine{psb\_dot}{Dot Product}
+\subroutine{psb\_gedot}{Dot Product}
 
 This function computes dot product between two vectors $x$ and
 $y$.\\
@@ -118,17 +118,17 @@ where:
 \item[$y$] represents the global subvector $y_{:,jy}$
 \end{description}
 
-\syntax{psb\_dot}{x, y, desc\_a, info}
+\syntax{psb\_gedot}{x, y, desc\_a, info}
-\syntax*{psb\_dot}{x, y, desc\_a, info, jx, jy}
+\syntax*{psb\_gedot}{x, y, desc\_a, info, jx, jy}
 \begin{table}[h]
 \begin{center}
 \begin{tabular}{ll}
 \hline
 $dot$, $x$, $y$ & {\bf Function}\\
 \hline
-Single Precision Real & psb\_dot\\
+Single Precision Real & psb\_gedot\\
-Long Precision Real & psb\_dot \\
+Long Precision Real & psb\_gedot \\
-Long Precision Complex & psb\_dot \\	
+Long Precision Complex & psb\_gedot \\	
 \hline
 \end{tabular}
 \end{center}
@@ -184,7 +184,7 @@ An integer value that contains an error code.
 %
 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 
-\subroutine{psb\_dot}{Generalized Dot Product}
+\subroutine{psb\_gedot}{Generalized Dot Product}
 
 This subroutine computes a series of  dot products among the columns of
 two dense matrices  $x$ and $y$: 
@@ -194,16 +194,16 @@ usual convention applies, i.e. the conjugate transpose of $x$ is
 used. If $x$ and $y$ are of rank one, then $res$ is a scalar, else it
 is a rank one array. 
 
-\syntax{psb\_dot}{res, x, y, desc\_a, info}
+\syntax{psb\_gedot}{res, x, y, desc\_a, info}
 \begin{table}[h]
 \begin{center}
 \begin{tabular}{ll}
 \hline
 $res$, $x$, $y$ & {\bf Subroutine}\\
 \hline
-Single Precision Real & psb\_dot\\
+Single Precision Real & psb\_gedot\\
-Long Precision Real & psb\_dot \\
+Long Precision Real & psb\_gedot \\
-Long Precision Complex & psb\_dot \\	
+Long Precision Complex & psb\_gedot \\	
 \hline
 \end{tabular}
 \end{center}
@@ -248,7 +248,7 @@ An integer value that contains an error code.
 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 
 
-\subroutine{psb\_amax}{Infinity-Norm of Vector}    
+\subroutine{psb\_geamax}{Infinity-Norm of Vector}    
 
 This function computes 
  the infinity-norm of a vector $x$.\\
@@ -262,8 +262,8 @@ where:
 \item[$x$] represents the global subvector $x_{:,jx}$
 \end{description}
 
-\syntax{psb\_amax}{x, desc\_a, info}
+\syntax{psb\_geamax}{x, desc\_a, info}
-\syntax*{psb\_amax}{x, desc\_a, info, jx}
+\syntax*{psb\_geamax}{x, desc\_a, info, jx}
 
 \begin{table}[h]
 \begin{center}
@@ -271,9 +271,9 @@ where:
 \hline
 $amax$ & $x$ & {\bf Function}\\
 \hline
-Single Precision Real&Single Precision Real & psb\_amax\\
+Single Precision Real&Single Precision Real & psb\_geamax\\
-Long Precision Real&Long Precision Real & psb\_amax \\
+Long Precision Real&Long Precision Real & psb\_geamax \\
-Long Precision Real&Long Precision Complex & psb\_zamax \\
+Long Precision Real&Long Precision Complex & psb\_zgeamax \\
 \hline
 \end{tabular}
 \end{center}
@@ -317,22 +317,22 @@ An integer value that contains an error code.
 %
 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 
-\subroutine{psb\_amax}{Generalized Infinity Norm}
+\subroutine{psb\_geamax}{Generalized Infinity Norm}
 
 This subroutine computes a series of  infinity norms on the columns of
 a  dense matrix  $x$: 
 \[ res(i) \leftarrow \max_k |x(k,i)| \]
 
-\syntax{psb\_amax}{res, x, desc\_a, info}
+\syntax{psb\_geamax}{res, x, desc\_a, info}
 \begin{table}[h]
 \begin{center}
 \begin{tabular}{lll}
 \hline
 $res$&  $x$& {\bf Subroutine}\\
 \hline
-Single Precision Real  &Single Precision Real  & psb\_amax\\
+Single Precision Real  &Single Precision Real  & psb\_geamax\\
-Long Precision Real    &Long Precision Real    & psb\_amax\\
+Long Precision Real    &Long Precision Real    & psb\_geamax\\
-Long Precision Real &Long Precision Complex & psb\_amax\\	
+Long Precision Real &Long Precision Complex & psb\_geamax\\	
 \hline
 \end{tabular}
 \end{center}
@@ -370,7 +370,7 @@ An integer value that contains an error code.
 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 
 
-\subroutine{psb\_asum}{1-Norm of Vector}    
+\subroutine{psb\_geasum}{1-Norm of Vector}    
 
 This function computes the 1-norm of a vector $x$.\\
 If $x$ is double precision real or single precision real vector
@@ -383,8 +383,8 @@ where:
 \item[$x$] represents the global subvector $x_{:,jx}$
 \end{description}
 
-\syntax{psb\_asum}{x, desc\_a, info}
+\syntax{psb\_geasum}{x, desc\_a, info}
-\syntax*{psb\_asum}{x, desc\_a, info, jx}
+\syntax*{psb\_geasum}{x, desc\_a, info, jx}
 
 \begin{table}[h]
 \begin{center}
@@ -392,9 +392,9 @@ where:
 \hline
 $dot$, $x$, $y$ & {\bf Function}\\
 \hline
-Single Precision Real & psb\_asum\\
+Single Precision Real & psb\_geasum\\
-Long Precision Real & psb\_asum \\
+Long Precision Real & psb\_geasum \\
-Long Precision Complex & psb\_asum \\	
+Long Precision Complex & psb\_geasum \\	
 \hline
 \end{tabular}
 \end{center}
@@ -440,7 +440,7 @@ An integer value that contains an error code.
 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 
 
-\subroutine {psb\_nrm2}{2-Norm of Vector}    
+\subroutine {psb\_genrm2}{2-Norm of Vector}    
 
 This function computes the 2-norm of a vector $x$.\\
 If $x$ is double precision real or single precision real vector
@@ -459,17 +459,17 @@ where:
 \hline
 $nrm2$, $x$ & {\bf Function}\\
 \hline
-Single Precision Real & psb\_nrm2\\
+Single Precision Real & psb\_genrm2\\
-Long Precision Real & psb\_nrm2 \\
+Long Precision Real & psb\_genrm2 \\
-Long Precision Complex & psb\_nrm2 \\
+Long Precision Complex & psb\_genrm2 \\
 \hline
 \end{tabular}
 \end{center}
 \caption{Data types\label{tab:f90nrm2}}
 \end{table}
 
-\syntax{psb\_nrm2}{x, desc\_a, info}
+\syntax{psb\_genrm2}{x, desc\_a, info}
-\syntax*{psb\_nrm2}{x, desc\_a, info, jx}
+\syntax*{psb\_genrm2}{x, desc\_a, info, jx}
 \begin{description}
 \item[\bf On Entry]
 \item[x] the local portion of global dense matrix
@@ -509,7 +509,7 @@ An integer value that contains an error code.
 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 
 
-\subroutine{psb\_nrmi}{Infinity Norm of Sparse Matrix}    
+\subroutine{psb\_spnrmi}{Infinity Norm of Sparse Matrix}    
 
 This function computes the infinity-norm of a matrix $A$:\\
 
@@ -525,16 +525,16 @@ where:
 \hline
 $nrmi$, $A$ & {\bf Function}\\
 \hline
-Single Precision Real & psb\_nrmi\\
+Single Precision Real & psb\_spnrmi\\
-Long Precision Real & psb\_nrmi \\
+Long Precision Real & psb\_spnrmi \\
-Long Precision Complex & psb\_nrmi \\
+Long Precision Complex & psb\_spnrmi \\
 \hline
 \end{tabular}
 \end{center}
 \caption{Data types\label{tab:f90nrmi}}
 \end{table}
 
-\syntax{psb\_nrmi}{A, desc\_a, info}
+\syntax{psb\_spnrmi}{A, desc\_a, info}
 
 \begin{description}
 \item[\bf On Entry]

docs/pdf/title.tex

@@ -5,7 +5,7 @@
 
 \ifx\pdfoutput\undefined % We're not running pdftex
 \else
-\pdfbookmark{Title Page}{title}
+\pdfbookmark{PSBLAS-v2.0 User's Guide}{title}
 \fi
 \newlength{\centeroffset}
 \setlength{\centeroffset}{-0.5\oddsidemargin}

docs/pdf/toolsrout.tex

@@ -1,4 +1,5 @@
 \section{Data management and initialization routines}
+\label{sec:toolrout}
 %
 %%   psb_alloc %%
 %

docs/pdf/userguide.tex

@@ -1,5 +1,6 @@
 \documentclass[12pt,a4paper,twoside]{article}
 \usepackage{pstricks}
+\usepackage{fancybox}
 \usepackage{amsfonts}
 % \usepackage{minitoc}
 % \setcounter{minitocdepth}{2}
@@ -63,9 +64,9 @@
 \newcommand{\example}{\stepcounter{example}%
 \section*{\examplename~\theexample}}
 
-\newcommand{\precdata}{\hyperlink{precdata}{{\tt psb\_prec\_data}}}
+\newcommand{\precdata}{\hyperlink{precdata}{{\tt psb\_prec\_type}}}
-\newcommand{\descdata}{\hyperlink{descdata}{{\tt psb\_desc\_data}}}
+\newcommand{\descdata}{\hyperlink{descdata}{{\tt psb\_desc\_type}}}
-\newcommand{\spdata}{\hyperlink{spdata}{{\tt psb\_spmat\_data}}}
+\newcommand{\spdata}{\hyperlink{spdata}{{\tt psb\_spmat\_type}}}
 
 \begin{document}
 \include{title}
@@ -82,7 +83,6 @@
 \pagenumbering{arabic}  % Arabic numbering
 \setcounter{page}{1}    % Chapters start on page 1
 
-\precdata
 \include{intro}
 \include{datastruct}
 \include{psbrout}