Algorithms Sequential and Parallel: A Unified Approach

The book represents a very decent approach for a transition from the sequential algorithms design ( RAM model ) to parallel algorithms for different models of parallel machines( not only SMP for which most of the software engineers get accustomed ). The book is an introduction for a person with a good background in a sequential algorithms design. The proof of the Master Theorem is somehow overcomplicated - it would have been better if there had been a sketch of the proof before the authors delve into the mathematically rigorous part. Before reading the proof in this book I recommend to read a proof of a simplified version of the Master Theorem as given in "Algorithms" by Dasgupta, Papadimitriou and Vazirani - it takes less than a page compared with 11 pages for the complete case in this book. I first came across the first edition's translation on Russian nearly 2 years ago ( the author's site says it was translated in 2007, but actually it was in the first quarter of 2006 ), then I bought the original 2nd edition - the book worth it. P.S. I've found nothing about Python language, as one of the review says, the authors use easily understandable pseudo-language. The book is not a cook book with code - this is virtually impossible for such a type of book as an algorithm realization heavily depends on a parallel machine architecture.

Have you been trying to find a way of to integrate the presentation of sequential and parallel algorithms? If you are, this book is for you! Authors Russ Miller and Laurence Boxer, have done an outstanding job of writing a great book on how to employ a philosophy of presenting a paradigm, such as divide and conquer, and then discussing implementation issues for both sequential and parallel models. Miller and Laurence Boxer begin by introducing the concept of asymptotic analysis. Next, the authors explain the Python programming language to write scripts. Then, they focus on fundamentals of induction and recursion. The authors continue by presenting the Master Method, a very useful cookbook-type of system for evaluating recurrence equations that are common in an algorithms-based setting. In addition, the authors next present an overview of combinational circuits and sorting networks. They also introduce fundamental models of computation, including the RAM and a variety of parallel models of computation. Next, the authors focus on the important problem of matrix multiplication, which is considered for a variety of models of computation. Then, they introduce the parallel prefix operation. The authors continue by introducing pointer jumping techniques and show how some list-based algorithms can be efficiently implemented in parallel. In addition, the authors next present the powerful divide and conquer paradigm. They also focus on two important application areas, namely, Computational Geometry and Image Processing. Next, the authors focus on fundamental graph theoretic problems. Finally, they cover sequential algorithms for polynomial evaluation and approximation of definite integrals. Due to the fact that authors of this excellent book present design and analysis of paradigms for sequential and parallel models, the reader will notice that the number of paradigms that can be treated is limited. But, that limitation is of no consequence when compared to a traditional sequential algorithms text.

I'm coauthor, so I'm not unbiased. Having said that.... The dominant textbook in the field of computer algorithms is Introduction to Algorithms, by Cormen et al. This is a very fine book. However, we have written Algorithms Sequential and Parallel in a very different style, which we feel will give significant advantages to many who use our book. Points of difference between these texts include the following: 1. Algorithms Sequential and Parallel has a unified approach to the presentation of sequential and parallel algorithms. Students of 21st Century computing will need to learn parallel algorithms, which are often closely related to their sequential analogs. Ours is the first algorithms text to integrate presentation of sequential and parallel algorithms so that readers can understand their relationships. This integrated treatment also frees the instructor from the common practice of spending most of an algorithms course on the study of sequential algorithms, with the last 2 or 3 weeks devoted to parallel algorithms - a practice that may mislead students into thinking that parallel computing is still the immature specialization of researchers, rather than a core technology for the 21st Century. 2. Algorithms Sequential and Parallel does not compete with Cormen et al. in the scope of topics covered. The Cormen et al. text, whose 2nd edition is well over 1100 pages, seems designed for at least a year sequence in the study of algorithms. By contrast, Algorithms Sequential and Parallel, 2nd edition, is under 400 pages. This makes it suitable for a one-semester study of algorithms, appropriate for many undergraduate and first- or second-year graduate programs; also, it makes Algorithms Sequential and Parallel significantly less costly. Algorithms Sequential and Parallel discusses mathematical tools used in the analysis of algorithms, a variety of sequential and parallel models of computation (including the RAM, PRAM, linear array, mesh, hypercube, pyramid, mesh-of-trees, and coarse-grained models), fundamental algorithms (including broadcast, semigroup computations, parallel prefix, sorting, searching) and their sequential and parallel implementations, paradigms such as recursive divide-and-conquer, and algorithms for a variety of applications areas (matrix operations and a variety of other numerical problems, computational geometry, image processing, graph problems, etc). Each chapter concludes with exercises at varying levels of difficulty. Note that a disinterested reviewer gave the first edition of Algorithms Sequential and Parallel a rating of 5 stars (out of 5) in SIGACT News 34 #2, June, 2003, pp. 3-5.