Parallelism in Matrix Computations - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Parallelism in Matrix Computations write by Efstratios Gallopoulos. This book was released on 2015-07-25. Parallelism in Matrix Computations available in PDF, EPUB and Kindle. 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 sparse matrix 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.
Parallel Algorithms for Matrix Computations
Parallel Algorithms for Matrix Computations - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Parallel Algorithms for Matrix Computations write by K. Gallivan. This book was released on 1990-01-01. Parallel Algorithms for Matrix Computations available in PDF, EPUB and Kindle. Describes a selection of important parallel algorithms for matrix computations. Reviews the current status and provides an overall perspective of parallel algorithms for solving problems arising in the major areas of numerical linear algebra, including (1) direct solution of dense, structured, or sparse linear systems, (2) dense or structured least squares computations, (3) dense or structured eigenvaluen and singular value computations, and (4) rapid elliptic solvers. The book emphasizes computational primitives whose efficient execution on parallel and vector computers is essential to obtain high performance algorithms. Consists of two comprehensive survey papers on important parallel algorithms for solving problems arising in the major areas of numerical linear algebra--direct solution of linear systems, least squares computations, eigenvalue and singular value computations, and rapid elliptic solvers, plus an extensive up-to-date bibliography (2,000 items) on related research.
Synthesis of Parallel Algorithms
Synthesis of Parallel Algorithms - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Synthesis of Parallel Algorithms write by John H. Reif. This book was released on 1993. Synthesis of Parallel Algorithms available in PDF, EPUB and Kindle. Mathematics of Computing -- Parallelism.
R Programming for Data Science
R Programming for Data Science - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook R Programming for Data Science write by Roger D. Peng. This book was released on 2012-04-19. R Programming for Data Science available in PDF, EPUB and Kindle. Data science has taken the world by storm. Every field of study and area of business has been affected as people increasingly realize the value of the incredible quantities of data being generated. But to extract value from those data, one needs to be trained in the proper data science skills. The R programming language has become the de facto programming language for data science. Its flexibility, power, sophistication, and expressiveness have made it an invaluable tool for data scientists around the world. This book is about the fundamentals of R programming. You will get started with the basics of the language, learn how to manipulate datasets, how to write functions, and how to debug and optimize code. With the fundamentals provided in this book, you will have a solid foundation on which to build your data science toolbox.
Parallel and Distributed Computation: Numerical Methods
Parallel and Distributed Computation: Numerical Methods - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Parallel and Distributed Computation: Numerical Methods write by Dimitri Bertsekas. This book was released on 2015-03-01. Parallel and Distributed Computation: Numerical Methods available in PDF, EPUB and Kindle. This highly acclaimed work, first published by Prentice Hall in 1989, is a comprehensive and theoretically sound treatment of parallel and distributed numerical methods. It focuses on algorithms that are naturally suited for massive parallelization, and it explores the fundamental convergence, rate of convergence, communication, and synchronization issues associated with such algorithms. This is an extensive book, which aside from its focus on parallel and distributed algorithms, contains a wealth of material on a broad variety of computation and optimization topics. It is an excellent supplement to several of our other books, including Convex Optimization Algorithms (Athena Scientific, 2015), Nonlinear Programming (Athena Scientific, 1999), Dynamic Programming and Optimal Control (Athena Scientific, 2012), Neuro-Dynamic Programming (Athena Scientific, 1996), and Network Optimization (Athena Scientific, 1998). The on-line edition of the book contains a 95-page solutions manual.
