The era of practical parallel programming has arrived, marked by the popularity of the MPI and OpenMP software standards and the emergence of commodity clusters as the hardware platform of choice for an increasing number of organizations. This exciting new book, Parallel Programming in C with MPI and OpenMP addresses the needs of students and professionals who want to learn how to design, analyze, implement, and benchmark parallel programs in C using MPI and/or OpenMP. It introduces a rock-solid design methodology with coverage of the most important MPI functions and OpenMP directives. It also demonstrates, through a wide range of examples, how to develop parallel programs that will execute efficiently on today�s parallel platforms. . .
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Most Helpful Customer Reviews
11 of 11 people found the following review helpful:
5.0 out of 5 stars
A good introductory book,
By A Customer
This review is from: Parallel Programming in C with MPI and OpenMP (Hardcover)
I used this book in my parallel computing course. It is really a good introductory book on parallel programming. Some examples are given in the book. The first seven chapters discuss parallel algorithm design, MPI, examples on The sieve of Eratosthenes and Floyd's algorithm, and performance analysis. Some other topics that also covered (some of these may or may not have working examples) include the matrix-vector multiplication, Monte Carlo, matrix multiplication, solving linear systems, sorting, FFT, search, and finite difference methods. Since there are so many topics to be covered in a book of only 500 pages, the discussions on most of these topics are not very depth. Overall, I still recommend this book for beginners in parallel algorithm design.
8 of 9 people found the following review helpful:
5.0 out of 5 stars
Very clear writing and good solid information,
By
This review is from: Parallel Programming in C with MPI and OpenMP (Hardcover)
The thing that impressed me was that the writing was so clear. True, the sentences tend to be short, but that is high virtue in technical writing. My students have been very positive about the book.
I also think the mathematical analysis is good, too, not too easy but not super hard either. This book just pulls together all the crucial information between two covers. I find myself agreeing almost exactly with where he places his "key" symbols in the margins to highlight important sentences, which is also a good sign that the book is "right on."
5 of 7 people found the following review helpful:
3.0 out of 5 stars
Probably not worth the money,
By
This review is from: Parallel Programming in C with Mpi and Openmp (Paperback)
Well, to begin with, for a book that has "...programming in C..." in the title, there is very little C code in the text-- most is pseudocode. Most of the book is an analysis of various parallel algorithms, with very little instruction on how to use MPI. There are much better resources out there for learning MPI, as Quinn only covers about 30 of the over 100 functions in MPI, without all that much detail. Some of extremely important and necessary concepts of parallel programming are only mentioned in passing...such as load balancing. Most of the applications (with the exception of matrix operations) are simple and basic to the point of making me wonder why you would even bother parallelizing them...even as a pedagogical tool. The book does a very good job of analyzing algorithms, but calling it an "introduction to MPI" or even an "introduction to parallel programming" textbook is incorrect.
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Inside This Book
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First Sentence:
Are you one of those people for whom "fast" isn't fast enough? Read the first page Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
benchmark your program, agglomerate tasks, striped decomposition, datatype dtype, data dependence diagram, old datatype, parallel program solving, parallel quicksort algorithm, parallel program implementing, persistent communication request, graph communicator, pragma precedes, state space tree, pragma omp parallel, centralized multiprocessor, document profile vectors, parallel for pragma, parallel pragma, datum size, indirect topology, collective communication function, maximum speedup achievable, parallel sieve, scalability function, room assignment problem Key Phrases - Capitalized Phrases (CAPs): (learn more)
Monte Carlo, Sieve of Eratosthenes, Amdahl's Law, Gustafson-Barsis's Law, Cosmic Cube, Processors Figure, United States, High Performance Fortran, Department of Energy, Lawrence Livermore National Laboratory, Sun Enterprise Server, Current Time, Deep Blue, Moore's Law, Partitioning Our, Sam Loyd, Speedup Megaflops New!
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Front Cover | Table of Contents | First Pages | Index | Surprise Me!
Are you one of those people for whom "fast" isn't fast enough? Read the first page Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
benchmark your program, agglomerate tasks, striped decomposition, datatype dtype, data dependence diagram, old datatype, parallel program solving, parallel quicksort algorithm, parallel program implementing, persistent communication request, graph communicator, pragma precedes, state space tree, pragma omp parallel, centralized multiprocessor, document profile vectors, parallel for pragma, parallel pragma, datum size, indirect topology, collective communication function, maximum speedup achievable, parallel sieve, scalability function, room assignment problem Key Phrases - Capitalized Phrases (CAPs): (learn more)
Monte Carlo, Sieve of Eratosthenes, Amdahl's Law, Gustafson-Barsis's Law, Cosmic Cube, Processors Figure, United States, High Performance Fortran, Department of Energy, Lawrence Livermore National Laboratory, Sun Enterprise Server, Current Time, Deep Blue, Moore's Law, Partitioning Our, Sam Loyd, Speedup Megaflops New!
Books on Related Topics | Concordance | Text Stats Browse Sample Pages:
Front Cover | Table of Contents | First Pages | Index | Surprise Me!
Citations (learn more)
This book cites 54
books:
See all 54 books this book cites
- Computer Architecture: A Quantitative Approach, Second Edition by John L. Hennessy on page 22, page 59, and Back Matter
- Designing and Building Parallel Programs: Concepts and Tools for Parallel Software Engineering by Ian Foster on page 90, and Back Matter
- Cluster Computing by Rajkumar Buyya in Back Matter (1), and Back Matter (2)
- Numerical Solution of Partial Differential Equations: Finite Difference Methods (Oxford Applied Mathematics & Computing Science Series) by G. D. Smith on page 335, and Back Matter
- A Guide to Monte Carlo Simulations in Statistical Physics by David P. Landau on page 269, and Back Matter
See all 54 books this book cites
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