Iain S. Duff

A set of level 3 basic linear algebra subprograms

This paper describes an extension to the set of Basic Linear Algebra Subprograms. The extensions are targeted at matrix-vector operations that should provide for efficient and portable implementations of algorithms for high-performance computers

A Fully Asynchronous Multifrontal Solver Using Distributed Dynamic Scheduling

In this paper, we analyze the main features and discuss the tuning of the algorithms for the direct solution of sparse linear systems on distributed memory computers developed in the context of a long term European research project. The algorithms use a multifrontal approach and are especially designed to cover a large class of problems. The problems can be symmetric positive definite, general symmetric, or unsymmetric matrices, both possibly rank deficient, and they can be provided by the user in several formats. The algorithms achieve high performance by exploiting parallelism coming from the sparsity in the problem and that available for dense matrices. The algorithms use a dynamic distributed task scheduling technique to accommodate numerical pivoting and to allow the migration of computational tasks to lightly loaded processors. Large computational tasks are divided into subtasks to enhance parallelism. Asynchronous communication is used throughout the solution process to efficiently overlap communication with computation. We illustrate our design choices by experimental results obtained on an SGI Origin 2000 and an IBM SP2 for test matrices provided by industrial partners in the PARASOL project.

An Approximate Minimum Degree Ordering Algorithm

An approximate minimum degree (AMD) ordering algorithm for preordering a symmetric sparse matrix prior to numerical factorization is presented. We use techniques based on the quotient graph for matrix factorization that allow us to obtain computationally cheap bounds for the minimum degree. We show that these bounds are often equal to the actual degree. The resulting algorithm is typically much faster than previous minimum degree ordering algorithms and produces results that are comparable in quality with the best orderings from other minimum degree algorithms.

An Updated Set of Basic Linear Algebra Subprograms (BLAS)

L. SUSAN BLACKFORD Myricom, Inc. JAMES DEMMEL University of California, Berkeley JACK DONGARRA The University of Tennessee IAIN DUFF Rutherford Appleton Laboratory and CERFACS SVEN HAMMARLING Numerical Algorithms Group, Ltd. GREG HENRY Intel Corporation MICHAEL HEROUX Sandia National Laboratories LINDA KAUFMAN William Patterson University ANDREW LUMSDAINE Indiana University ANTOINE PETITET Sun Microsystems ROLDAN POZO National Institute of Standards and Technology KARIN REMINGTON The Center for Advancement of Genomics and R. CLINT WHALEY Florida State University

An Unsymmetric-Pattern Multifrontal Method for Sparse LU Factorization

Sparse matrix factorization algorithms for general problems are typically characterized by irregular memory access patterns that limit their performance on parallel-vector supercomputers. For symmetric problems, methods such as the multifrontal method avoid indirect addressing in the innermost loops by using dense matrix kernels. However, no efficient LU factorization algorithm based primarily on dense matrix kernels exists for matrices whose pattern is very unsymmetric. We address this deficiency and present a new unsymmetric-pattern multifrontal method based on dense matrix kernels. As in the classical multifrontal method, advantage is taken of repetitive structure in the matrix by factorizing more than one pivot in each frontal matrix, thus enabling the use of Level 2 and Level 3 BLAS. The performance is compared with the classical multifrontal method and other unsymmetric solvers on a CRAY C-98.

Solving Linear Systems on Vector and Shared Memory Computers

Vector and parallel processing overview of current high-performance computers implementation details and overhead performance - analysis, modeling and measurements building blocks in linear algebra direct solution of sparse linear systems iterative solution of sparse linear systems. Appendices: acquiring mathematical software information on various high-performance computers level 1,2, and 3 BLAS quick reference operation counts for various BLAS and decompositions.

The effect of ordering on preconditioned conjugate gradients

We investigate the effect of the ordering of the unknowns on the convergence of the preconditioned conjugate gradient method. We examine a wide range of ordering methods including nested dissection, minimum degree, and red-black and consider preconditionings without fill-in. We show empirically that there can be a significant difference in the number of iterations required by the conjugate gradient method and suggest reasons for this marked difference in performance.We also consider the effect of orderings when an incomplete factorization which allows some fill-in is performed. We consider the effect of automatically controlling the sparsity of the incomplete factorization through drop tolerances and level of fill-in.

A survey of sparse matrix research

This paper surveys the state of the art in sparse matrix research in January 1976. Much of the survey deals with the solution of sparse simultaneous linear equations, including the storage of such matrices and the effect of paging on sparse matrix algorithms. In the symmetric case, relevant terms from graph theory are defined. Band systems and matrices arising from the discretization of partial differential equations are treated as separate cases. Preordering techniques are surveyed with particular emphasis on partitioning (to block triangular form) and tearing (to bordered block triangular form). Methods for solving the least squares problem and for sparse linear programming are also reviewed. The sparse eigenproblem is discussed with particular reference to some fairly recent iterative methods. There is a short discussion of general iterative techniques, and reference is made to good standard texts in this field. Design considerations when implementing sparse matrix algorithms are examined and finally comments are made concerning the availability of codes in this area.

Algorithm 837: AMD, an approximate minimum degree ordering algorithm

AMD is a set of routines that implements the approximate minimum degree ordering algorithm to permute sparse matrices prior to numerical factorization. There are versions written in both C and Fortran 77. A MATLAB interface is included.

On Algorithms For Permuting Large Entries to the Diagonal of a Sparse Matrix

We consider bipartite matching algorithms for computing permutations of a sparse matrix so that the diagonal of the permuted matrix has entries of large absolute value. We discuss various strategies for this and consider their implementation as computer codes. We also consider scaling techniques to further increase the relative values of the diagonal entries. Numerical experiments show the effect of the reorderings and the scaling on the solution of sparse equations by a direct method and by preconditioned iterative techniques.