Parallelism in Matrix Computations

This book is primarily intended as a research monograph that could also be used in graduate courses for the design of parallel algorithms in matrix computations.

It assumes general but not extensive knowledge of numerical linear algebra, parallel architectures, and parallel programming paradigms.

The book consists of four parts: (I) Basics; (II) Dense and Special Matrix Computations; (III) Sparse Matrix Computations; and (IV) Matrix functions and characteristics. Part I deals with parallel programming paradigms and fundamental kernels, including reordering schemes for sparse matrices. Part II is devoted to dense matrix computations such as parallel algorithms for solving linear systems, linear least squares, the symmetric algebraic eigenvalue problem, and the singular-value decomposition. It also deals with the development of parallel algorithms for special linear systems such as banded ,Vandermonde ,Toeplitz ,and block Toeplitz systems. Part III addresses sparsematrix computations: (a) the development of parallel iterative linear system solvers with emphasis on scalable preconditioners, (b) parallel schemes for obtaining a few of the extreme eigenpairs or those contained in a given interval in the spectrum of a standard or generalized symmetric eigenvalue problem, and (c) parallel methods for computing a few of the extreme singular triplets. Part IV focuses on the development of parallel algorithms for matrix functions and special characteristics such as the matrix pseudospectrum and the determinant. The book also reviews the theoretical and practical background necessary when designing these algorithms and includes an extensive bibliography that will be useful to researchers and students alike.

The book brings together many existing algorithms for the fundamental matrix computations that have a proven track record of efficient implementation in terms of data locality and data transfer on state-of-the-art systems, as well as several algorithms that are presented for the first time, focusing on the opportunities for parallelism and algorithm robustness.

• BLAS computing
• Davidson methods
• Dense matrix computations
• GramSchmidt orthogonalization
• Grid based methods
• LU factorization
• Lanczos method
• Matrix dimensionality reduction
• Sparse matrix computations
• Trace minimization method

“The exposition of the material is always very clear. The book is written confidently, with the greatest expertise and a remarkable breadth of topics. It should be an excellent resource for a broad audience of applied mathematicians. I would recommended it to all students, engineers and researchers in applied mathematics who wish to learn something about modern parallel techniques for large-scale matrix computation. Kudos to the authors on having produced such a delightful read and a much-needed reference!” (Bruno Carpentieri, Mathematical Reviews, 2017)

“The goal of this book is to provide basic principles for the design of such efficient parallel algorithms for dense and sparse matrices. … The book is intended to be adequate for researchers as well as for advanced graduates.” (Gudula Rünger, zbMATH 1341.65011, 2016)

“This book covers parallel algorithmsfor a wide range of matrix computation problems, ranging from solving systems of linear equations to computing pseudospectra of matrices. … This is a valuable reference book for researchers and practitioners in parallel computing. It includes up-to-date and comprehensive lists of references for various topics. … this book is well written and accurate. I highly recommend it to the parallel computing community … .” (Sanzheng Qiao, Computing Reviews, November, 2015)

• Computer Engineering and Informatics, University of Patras, Patras, Greece

  Efstratios Gallopoulos

• Campus de Beaulieu, INRIA/IRISA, Rennes Cedex, France

  Bernard Philippe

• Dept. of Computer Science, Purdue University, West Lafayette, USA

  Ahmed H. Sameh

Efstratios Gallopoulos, University of Patras, Patras Greece
Bernard Philippe, INRIA/IRISA, Rennes Cedex, France
Ahmed H. Sameh, Purdue University, West Lafayette, IN, USA

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