Parallel Programming: for Multicore and Cluster Systems

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Product Description

Innovations in hardware architecture, like hyper-threading or multicore processors, mean that parallel computing resources are available for inexpensive desktop computers. In only a few years, many standard software products will be based on concepts of parallel programming implemented on such hardware, and the range of applications will be much broader than that of scientific computing, up to now the main application area for parallel computing.

Rauber and R nger take up these recent developments in processor architecture by giving detailed descriptions of parallel programming techniques that are necessary for developing efficient programs for multicore processors as well as for parallel cluster systems and supercomputers. Their book is structured in three main parts, covering all areas of parallel computing: the architecture of parallel systems, parallel programming models and environments, and the implementation of efficient application algorithms. The emphasis lies on parallel programming techniques needed for different architectures.

The main goal of the book is to present parallel programming techniques that can be used in many situations for many application areas and which enable the reader to develop correct and efficient parallel programs. Many examples and exercises are provided to show how to apply the techniques. The book can be used as both a textbook for students and a reference book for professionals. The presented material has been used for courses in parallel programming at different universities for many years.

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Parallel Programming: for Multicore and Cluster Systems Review

This book is great academic quality survey of modern parallel programming. It starts with a brief and yet thorough overview of architecture and recent innovations of the multicore processors. To name a few, the book picks a glance at Intel, AMD, Sun T-series, and IBM Power processors. Followed by that, a short introduction into performance analysis as well as parallel design patterns are presented. Numerous references (often academic journals) are also provided throughout the book.

The book then quickly reviews MPI-1 and MPI-2, which are the most popular Distributed Memory models. I'd taken courses in parallel programming back in the graduate school and used both MPI and OpenMP; however this brief chapter on message passing was simply incredible. Shared memory using POSIX threads and a hint at how to implement different parallel patterns (such as consumer-producer, pipelining, thread pools, and task pools) are followed. The book goes further and addresses Java threads and OpenMP. The final chapter is algorithms for linear systems and works out a few examples like Gaussian elimination...

I found it an invaluable up to date book. Yet it's a survey of parallel programming, aiming at the more experienced programmers, either coming from industry or scientific computing world.

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