Average Reviews:
(More customer reviews)It came as somewhat of a surprise to the industry that coupling together several PC's enabled the construction of what was in effect a supercomputer at a small fraction of the cost.
What began twenty or so years ago has now influenced the design of CPU's and the intereconnection 'LANs' that facilitate the transfer of data between the processors. And this clearly hasn't stopped. The AMD Opteron CPU's and Intel's PCI-Express are simply the latest innovations in silicon, and more is coming.
From a system architecture standpoint, we have (and the book discusses) clusters, Grids, and distributed processor systems -- all of which are fairly loosely defined with plenty of room for very good discussions over several beer.
What this book brings is an excellent introduction into the state of the art in parallel computers as it exists today. As is often the case with books that are pushing the state of the art, it is written by a large numnber of experts and edited together. Each chapter covers a particular area in depth from the design of the hardware to the languages (primarily Fortran and Java), to the solution of a series of common problems that are frequent in several different application areas.
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.
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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.
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