\documentclass[leqno,12pt]{article}     % latex2e
\usepackage{amsmath}                     % latex2e

\def\SPARQR{\mbox{\sf SPARQR}}
\def\LORA{\mbox{\sf LORA}}
\def\O2{\mbox{Origin 2000}}

\begin{document}

\title{\bf Parallel Performance of Several Codes for Solving
           Sparse Linear Systems on SGI-CRAY \O2\thanks{
        This work was supported in part by NSF grants CCR-9619763
        and ECS-9527123 (Grand Challenge) as well as by
        grant MU-MM 02/96 of the Bulgarian Ministery of Education and
        Science.}
}

\author{Tz. Ostromsky\thanks{Department of Computer Sciences,
        Purdue University, West Lafayette, IN 47907 - 1398,
        % \protect\\ {\em e-mail:}  tto@cs.purdue.edu}
        \quad {\em e-mail:}  tto@cs.purdue.edu}
   \and      A. Sameh\thanks{Department of Computer Sciences,
        Purdue University, West Lafayette, IN 47907 - 1398,
        % \protect\\ {\em e-mail:}  sameh@cs.purdue.edu}
        \quad {\em e-mail:}  sameh@cs.purdue.edu}
   \and     Z. Zlatev\thanks{National Environmental Research Institute,
        Frederiksborgvej 399, DK-4000 Roskilde, Denmark,
        % \protect\\ {\em e-mail:}  luzz@sun2.dmu.dk}
        \quad {\em e-mail:}  luzz@sun2.dmu.dk}
}
\date{}
\maketitle


\begin{abstract}

Parallel solution of sparse linear systems is a difficult task with very
important practical applications. Extensive research has recently been
done in this area and several codes have been introduced.
The increasing variety of multiprocessors available and the dynamic
developments, however, result in numerical experiments on different
machines for the variety of codes proposed.  % They are hardly comparable,
This causes certain difficulties in comparison of the codes themselves.
This work is aimed to show results and compare the performance of several
different sparse matrix codes on one and the same parallel platform
-- SGI-CRAY \O2. Results of the dense LAPACK solvers for the test
problems are also provided.

\vspace{4 mm}
\noindent
{\em Keywords:} linear system,
sparse matrix, block partitioning, bandwidth,
parallel algorithm, speed-up.
% shared memory, cache.

\end{abstract}

\end{document}