Introducation to Parallel Computing is a complete end-to-end source of information on almost all aspects of parallel computing from introduction to architectures to programming paradigms to algorithms to programming standards. It is the only book to have complete coverage of traditional Computer Science algorithms (sorting, graph and matrix algorithms), scientific computing algorithms (FFT, sparse matrix computations, N-body methods), and data intensive algorithms (search, dynamic programming, data-mining).
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From the Back Cover
Introduction to Parallel Computing, Second Edition
Ananth Grama
Anshul Gupta
George Karypis
Vipin Kumar
Increasingly, parallel processing is being seen as the only cost-effective method for the fast solution of computationally large and data-intensive problems. The emergence of inexpensive parallel computers such as commodity desktop multiprocessors and clusters of workstations or PCs has made such parallel methods generally applicable, as have software standards for portable parallel programming. This sets the stage for substantial growth in parallel software.
Data-intensive applications such as transaction processing and information retrieval, data mining and analysis and multimedia services have provided a new challenge for the modern generation of parallel platforms. Emerging areas such as computational biology and nanotechnology have implications for algorithms and systems development, while changes in architectures, programming models and applications have implications for how parallel platforms are made available to users in the form of grid-based services.
This book takes into account these new developments as well as covering the more traditional problems addressed by parallel computers. Where possible it employs an architecture-independent view of the underlying platforms and designs algorithms for an abstract model. Message Passing Interface (MPI), POSIX threads and OpenMP have been selected as programming models and the evolving application mix of parallel computing is reflected in various examples throughout the book.
* Provides a complete end-to-end source on almost every aspect of parallel computing (architectures, programming paradigms, algorithms and standards).
* Covers both traditional computer science algorithms (sorting, searching, graph, and dynamic programming algorithms) as well as scientific computing algorithms (matrix computations, FFT).
* Covers MPI, Pthreads and OpenMP, the three most widely used standards for writing portable parallel programs.
* The modular nature of the text makes it suitable for a wide variety of undergraduate and graduate level courses including parallel computing, parallel programming, design and analysis of parallel algorithms and high performance computing.
Ananth Grama is Associate Professor of Computer Sciences at Purdue University, working on various aspects of parallel and distributed systems and applications.
Anshul Gupta is a member of the research staff at the IBM T. J. Watson Research Center. His research areas are parallel algorithms and scientific computing.
George Karypis is Assistant Professor in the Department of Computer Science and Engineering at the University of Minnesota, working on parallel algorithm design, graph partitioning, data mining, and bioinformatics.
Vipin Kumar is Professor in the Department of Computer Science and Engineering and the Director of the Army High Performance Computing Research Center at the University of Minnesota. His research interests are in the areas of high performance computing, parallel algorithms for scientific computing problems and data mining.
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Most Helpful Customer Reviews
20 of 23 people found the following review helpful:
2.0 out of 5 stars
Better read Journals than this book,
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Amazon Verified Purchase(What's this?)
This review is from: Introduction to Parallel Computing (2nd Edition) (Hardcover)
I bought the book a few months ago as textbook for my semester class in high performance computing. After reading the first 3 chapters I realized that this book is a waste. The examples are only solved partially, a lot of jargons (they should have put the terminology in separate table, maybe).
I was hoping, by reading the book I'd learn something essential and got the basic philosophy of high-performance computing/parallel processing. Instead, I got more confused than before reading it! (I used to be real-time software programmer, so the field is not totally new to me). The authors tried to put everything in this small 633-pages book. Even my professor said it is useless to read the book and refer us to other papers [Robertazzi's papers], and yes, these IEEE/ACM papers are much clearly explained and understood! I also found that a website is much better explaining the concept. Another book is also I guess better: "Fundamentals of Parallel Processing" by Harry F. Jordan and Gita Alaghband. Don't waste your money on this book.
8 of 8 people found the following review helpful:
2.0 out of 5 stars
Too many mistakes.,
By Erik R. Knowles "EK" (Evergreen, CO) - See all my reviews
This review is from: Introduction to Parallel Computing (2nd Edition) (Hardcover)
I agree with the other reviewers who have said that this book is sloppy. There are just far too many mistakes for a 2nd edition book; very discouraging in an Addison-Wesley print.
The content is OK, and fairly thorough, but as another reviewer noted, there's considerable handwaving going on in some of the explanations. Bottom line: a cleaned-up 3rd edition could be a very good textbook. Too bad I'm stuck with the 2nd edition :(
17 of 20 people found the following review helpful:
2.0 out of 5 stars
A sloppily written book,
By A Customer
This review is from: Introduction to Parallel Computing (2nd Edition) (Hardcover)
The content should be accessible to any graduate student but the sloppy writing style has made it unnecessarily difficult to read. Out of the many poorly written places, here is an example. In section 6.3.5 on page 248, it wrote, "Recall from section 9.3.1..." But I am only in chapter 6, how can I recall something from chapter 9. I then checked chapter 9 and found out that the forward reference was not a typo. "Foundations of Multithreaded, Parallel, and Distributed Programming" by Gregory Andrews is a much better written book. Unfortunately, Gregory's book does not cover the same content.
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Inside This Book
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Key Phrases - Statistically Improbable Phrases (SIPs):
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search overhead factor, exploratory decomposition, asymptotic isoefficiency function, bitonic merging network, isoefficiency term, efficient parallel formulations, current best solution path, int npes, pyramidal mesh, int nlocal, parallel run time, present parallel formulations, sample sort algorithm, transpose algorithm, speculative decomposition, isoefficiency analysis, parenthesization problem, donor processor, asynchronous round robin, bitonic sort algorithm, parallel quicksort algorithm, various communication operations, sequential run time, serial formulation, int sendcount Key Phrases - Capitalized Phrases (CAPs): (learn more)
Parallel Programming Platforms, Dense Matrix Algorithms, Principles of Parallel Algorithm Design, Programming Shared Address Space Platforms, Graph Algorithms, Fast Fourier Transform, Programming Using the Message-Passing Paradigm, Analytical Modeling of Parallel Programs, Dynamic Programming, Repeat Problem, Message Passing Interface, Lake Superior, Consider Dijkstra, Communication Costs, Thread Basics, Basic Communication Operations, Scaled Speedup Browse Sample Pages:
Front Cover | Table of Contents | First Pages | Index | Back Cover | Surprise Me!
search overhead factor, exploratory decomposition, asymptotic isoefficiency function, bitonic merging network, isoefficiency term, efficient parallel formulations, current best solution path, int npes, pyramidal mesh, int nlocal, parallel run time, present parallel formulations, sample sort algorithm, transpose algorithm, speculative decomposition, isoefficiency analysis, parenthesization problem, donor processor, asynchronous round robin, bitonic sort algorithm, parallel quicksort algorithm, various communication operations, sequential run time, serial formulation, int sendcount Key Phrases - Capitalized Phrases (CAPs): (learn more)
Parallel Programming Platforms, Dense Matrix Algorithms, Principles of Parallel Algorithm Design, Programming Shared Address Space Platforms, Graph Algorithms, Fast Fourier Transform, Programming Using the Message-Passing Paradigm, Analytical Modeling of Parallel Programs, Dynamic Programming, Repeat Problem, Message Passing Interface, Lake Superior, Consider Dijkstra, Communication Costs, Thread Basics, Basic Communication Operations, Scaled Speedup Browse Sample Pages:
Front Cover | Table of Contents | First Pages | Index | Back Cover | Surprise Me!
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