This paper discusses the design of linear algebra libraries for high performance computers. Particular emphasis is placed on the development of scalable algorithms for MIMD distributed memory concurrent computers. A brief description of the EISPACK, LINPACK, and LAPACK libraries is given, followed by an outline of ScaLAPACK, which is a distributed memory version of LAPACK currently under development. The importance of block-partitioned algorithms in reducing the frequency of data movement between different levels of hierarchical memory is stressed. The use of such algorithms helps reduce the message startup costs on distributed memory concurrent computers. Other key ideas in our approach are the use of distributed versions of the Level 3 Basic Linear Algebra Subprograms (BLAS) as computational building blocks, and the use of Basic Linear Algebra Communication Subprograms (BLACS) as communication building blocks. Together the distributed BLAS and the BLACS can be used to construct highe...

This paper surveys the current state of applications of large dense numerical linear algebra, and the influence of parallel computing. Furthermore, we attempt to crystalize many important ideas that we feel have been sometimes been misunderstood in the rush to write fast programs. 1 Introduction This paper represents my continuing efforts to track the status of large dense linear algebra problems. The goal is to shatter the barriers that separate the various interested communities while commenting on the influence of parallel computing. A secondary goal is to crystalize the most important ideas that have all too often been obscured by the details of machines and algorithms. Parallel supercomputing is in the spotlight. In the race towards the proliferation of papers on person X's experiences with machine Y (and why his algorithm runs faster than person Z's), sometimes we have lost sight of the applications for which these algorithms are meant to be useful. This paper concentrates on la...

This paper discusses the design of linear algebra libraries for high performance computers. Particular emphasis is placed on the development of scalable algorithms for MIMD distributed memory concurrent computers. A brief description of the EISPACK, LINPACK, and LAPACK libraries is given, followed by an outline of ScaLAPACK, which is a distributed memory version of LAPACK currently under development. The importance of block-partitioned algorithms in reducing the frequency of data movementbetween di#erent levels of hierarchical memory is stressed. The use of such algorithms helps reduce the message startup costs on distributed memory concurrent computers. Other key ideas in our approach are the use of distributed versions of the Level 3 Basic Linear Algebra Subgrams #BLAS# as computational building blocks, and the use of Basic Linear Algebra Communication Subprograms #BLACS# as communication building blocks. Together the distributed BLAS and the BLACS can be used to construct ...

In the March 24, 1991 issue of NA Digest, I submitted a questionnaire asking who was solving large dense linear systems of equations. Based on the responses, nearly all large dense linear systems today arise from either the benchmarking of supercomputers or applications involving the influence of a two dimensional boundary on three dimensional space. Not surprisingly, the area of computational aerodynamics or aero-electromechanics represents an important commercial application requiring the solution of such systems. The largest unstructured matrix that has been factored using Gaussian Elimination was a complex matrix of size 55,296. The largest dense matrix solved on a Sun using an iterative method was a real matrix of size 20,000. It is unclear at this time whether dense methods are truly needed at all for huge matrices. It is intended to survey users every year with the hope of including more applications as I am made aware of them. 1 Introduction The idea to poll solvers of large d...

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In this paper we look at a number of approaches being investigated in the Center for Research on Parallel Computation (CRPC) to develop linear algebra software for high-performance computers. These approaches are exemplified by the LAPACK, templates, and ARPACK projects. LAPACK is a software library for performing dense and banded linear algebra computations, and was designed to run efficiently on high performance computers. We focus on the design of the distributed memory version of LAPACK, and on an object-oriented interface to LAPACK. The templates project aims at making the task of developing sparse linear algebra software simpler and easier. Reusable software templates are provided that the user can then customize to modify and optimize a particular algorithm, and hence build a more complex applications. ARPACK is a software package for solving large scale eigenvalue problems, and is based on an implicitly restarted variant of the Arnoldi scheme. The paper focuses on issues impact...

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this document and the corresponding documentations are in the public domain, and are available from netlib (http://www.netlib.org/) [DG87]. For instance, the EISPACK, LINPACK, LAPACK, BLACS, ScaLAPACK, and ATLAS software packages are in the public domain, and are available from netlib. Moreover, these publically available software packages can also be retrieved by e-mail. For example, to obtain more information on LAPACK, one should send the following oneline email message to netlib@ornl.gov: send index from lapack. Information for other packages can be similarly obtained. Real-time information on the NetSolve project can be found at the following web address http://www.cs.utk.edu/netsolve.

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