Handbook of Parallel Computing and Statistics

Erricos John Kontoghiorghes

“… 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

• General – Parallel Computing. A Brief Introduction to Parallel Computing. Parallel Computer Architecture. Fortran and Java for High-Performance Computing. Parallel Algorithms for the Singular Value Decomposition. Iterative Methods for the Partial Eigensolution of Symmetric Matrices on Parallel Machines. Optimization. Parallel Optimization Methods. Parallel Computing in Global Optimization. Nonlinear Optimization: A Parallel Linear Algebra Standpoint. Statistical Applications. On Some Statistical Methods for Parallel Computation. Parallel Algorithms for Predictive Modeling. Parallel Programs for Adaptive Designs. A Modular VLSI Architecture for the Real-Time Estimation of Higher Order Moments and Cumulants. Principal Component Analysis for Information Retrieval. Matrix Rank Reduction for Data Analysis and Feature Extraction. Parallel Computation in Econometrics: A Simplified Approach. Parallel Bayesian Computation. Index.