From dpk Sun Nov  7 21:19:44 1993
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	id AA13806; Sun, 7 Nov 93 21:19:42 EST
Date: Sun, 7 Nov 93 21:19:42 EST
From: dpk (David P. Koester)
Message-Id: <9311080219.AA04327@bugs.npac.syr.edu>
Received: by bugs.npac.syr.edu (4.1/N-0.12)
	id AA04327; Sun, 7 Nov 93 21:19:41 EST
To: paulc@npac
Subject: ctivities report (tex) - early for once...
Status: RO

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\begin{document}

\bigskip

\begin{center}
{\large \bf Activities Report for October --- November 1993} \\
{\large David Koester}
\end{center}
\bigskip

My activities for the October 1993 --- November 1993 reporting period has focused
on three areas:
\begin{enumerate}
\item developing a general block-diagonal-bordered form sparse Choleski solver
\item examining parallel dense matrix solvers for both Choleski factorization and general LU factorization with pivoting
\item becoming proficient with level-2 and level-3 Basic Linear Algebra Subroutines (BLAS) and (sequential) LAPACK routines
\end{enumerate}
All efforts in these three areas support both my Ph.D. research and
the NPAC --- Niagara Mohawk contract.

I am presently developing implementations of parallel sparse
block-diagonal-bordered linear solvers for the Thinking Machines CM-5.
The implementation of current interest is a sparse
block-diagonal-bordered Choleski solver for symmetric positive
definite matrices, such as would be used to solve load-flow equations.
My previous sparse LU factorization algorithm implementations for the
CM-5, inneffectually handled the last matrix block sequentially on the
host node of the CM-5.  After examining the characteristics of real
block-diagonal-bordered power system matrices when examining matrix
orderings, it was obvious that the last diagonal block in the matrix
was not sparse, and dense factorization techniques would be more
efficient than sparse factorization techniques because dense
techniques can use vector/matrix BLAS that are optimized for memory
access and consequently optimized for performance.

I have examined efficient parallel dense factorization algorithms to
be used to solve the last block in block-diagonal-bordered sparse
power systems matrices.  Previous NPAC research into FORTRAN
benchmarks has been utilized to develop efficient parallel dense
matrix solvers for the last-block in sparse matrices.  I have
developed two implementations of dense matrix factorization on the
CM-5:
\begin{enumerate}
\item dense Choleski factorization for symmetric positive definite matrices
\item dense LU factorization for general matrices that require pivoting to ensure numerical stability
\end{enumerate}
These dense factorization implementations are complete, as is the
software implementation for the sparse block-diagonal-bordered
Choleski solver.  This software is currently being tested and
debugged.  Development effort has been significantly decreased because
large portions of the code utilize existing kji-SAXPY routines from
the LAPACK software library.  However, significant effort was required
to rewrite the benchmark code because it was not developed using good
software engineering practices.  My software implementation also
includes features that permit variable numbers of CM-5 processors to
be used in the factorization.  In order to optimize performance and
increase portability of this software, standard BLAS and LAPACK
routines have been used whenever practical.  Approximately a week of
effort was expended while obtaining a working knowledge of these
powerful software libraries.

A request has been made to use actual Niagara Mohawk data for this
research, I am including two figures with this report to illustrate
the similarities of the public-domain data used to date, and an actual
Niagara Mohawk power system network obtained from Wm. Merritt (NiMo
Operations).  The first figure presents an ordering of the 1723 node
Boeing-Harwell BSCPWR09.PAS matrix, while the second figure depicts an
ordering of actual Niagara Mohawk power system data.  This Niagara
Mohawk power system network has 1766 nodes.  This recursive ordering is
for a maximum block sizes of 128 and 16 nodes.  Notice the
similarities in the matrices --- consequently, any of my research will
be directly applicable to the public-domain data or the limited access
Niagara Mohawk data.  I prefer to use public-domain for my research,
because of the fact that others can attempt to duplicate my results.

\end{document}
\endinput





From dpk Sun Nov  7 21:21:52 1993
Received: from bugs.npac.syr.edu by spica.npac.syr.edu (4.1/I-1.98K)
	id AA13813; Sun, 7 Nov 93 21:21:51 EST
Date: Sun, 7 Nov 93 21:21:50 EST
From: dpk (David P. Koester)
Message-Id: <9311080221.AA04329@bugs.npac.syr.edu>
Received: by bugs.npac.syr.edu (4.1/N-0.12)
	id AA04329; Sun, 7 Nov 93 21:21:49 EST
To: paulc@npac
Subject: activities report (attachments) - early for once...
Status: RO

Paul,

The attachments for the report have been sent to

	phaser (color)
	cezanne (B&W)

both printers are in Rich's area, so if you want
the printouts before I show up on Monday, please
pick them up.

Dave

