Chapman and Hall/CRC
552 pages | 69 B/W Illus.
Technological improvements continue to push back the frontier of processor speed in modern computers. Unfortunately, the computational intensity demanded by modern research problems grows even faster. Parallel computing has emerged as the most successful bridge to this computational gap, and many popular solutions have emerged based on its concepts, such as grid computing and massively parallel supercomputers. The Handbook of Parallel Computing and Statistics systematically applies the principles of parallel computing for solving increasingly complex problems in statistics research.
This unique reference weaves together the principles and theoretical models of parallel computing with the design, analysis, and application of algorithms for solving statistical problems. After a brief introduction to parallel computing, the book explores the architecture, programming, and computational aspects of parallel processing. Focus then turns to optimization methods followed by statistical applications. These applications include algorithms for predictive modeling, adaptive design, real-time estimation of higher-order moments and cumulants, data mining, econometrics, and Bayesian computation. Expert contributors summarize recent results and explore new directions in these areas.
Its intricate combination of theory and practical applications makes the Handbook of Parallel Computing and Statistics an ideal companion for helping solve the abundance of computation-intensive statistical problems arising in a variety of fields.
“… What this book brings is an excellent introduction into the state of the art in parallel computers as it exists today. … This book is an excellent summary of parallel computing as it exists today. It would be of particular help to the person responsible for writing the proposal for an organization to buy/build one. The book is probably a bit too advanced for a course at an undergraduate level, but would be excellent for first year graduate students in a wide variety of fields from computer science to bio-informatics, data mining, cryptography or any number of other fields requiring heavy duty computation.”
— In Books-On-Line
“[The book’s] chapters cover reasonably well the different domains where parallel computing is required and applied. The general introduction is clear and sound. There is some overlapping in the introduction of several chapters, which … makes the reading of a particular chapter easier. Overall, the balance between general introduction, problem-specific information, and applications is well equilibrated.
…I am convinced that this handbook is of interest to a large community of researchers, students, and practitioners (dealing with computational methods in any domain) as the book covers a wide range of applications where parallel computing is of great actuality. The book will guide them in the analysis [of] whether a particular computational problem is feasible for a parallelization and if this is the case, help them to realize it. With respect to this, the extensive bibliography included in the handbook is particularly precious…”
—Manfred Gilli, Professor, Department of Econometrics, University of Geneva, Switzerland
A Brief Introduction to Parallel Computing; M. Paprzycki and P. Stpiczyński
Parallel Computer Architecture; T. Trancoso and P. Evripidou
Fortran and Java for High-Performance Computing; H. Perrott, C. Phillipe and T. Stitt
Parallel Algorithms for the Singular Value Decomposition; M.W. Berry, D. Mezher, B. Philippe and A. Sameh
Iterative Methods for the Partial Eigensolution of Symmetric Matrices on Parallel Machines; M. Clint
Parallel Optimization Methods; Y. Censor and S.A. Zenios
Parallel Computing in Global Optimization; M. D’Apuzzo, M. Marino, A. Migdalas, P.M. Pardalos and G. Toraldo
Nonlinear Optimization: A Parallel Linear Algebra Standpoint; M. D’Apuzzo, M. Marino, A. Migdalas and P.M. Pardalos
On Some Statistical Methods for Parallel Computation; E.J. Wegman
Parallel Algorithms for Predictive Modeling; M. Hegland
Parallel Programs for Adaptive Designs; Q.F. Stout and J. Hardwick
A Modular VLSI Architecture for the Real-Time Estimation of Higher Order Moments and Cumulants; S. Manolakos
Principal Component Analysis for Information Retrieval; M.W. Berry and D.I. Martin
Matrix Rank Reduction for Data Analysis and Feature Extraction; H. Park and L. Elden
Parallel Computation in Econometrics: A Simplified Approach; J.A. Doornik, N. Shephard and D.F. Hendry
Parallel Bayesian Computation; D.J. Wilkinson