Kategoria BISAC: Computers >> Programming - Parallel

If you're looking to take full advantage of multi-core processors with concurrent programming, this practical book provides the knowledge and hands-on experience you need. The Art of Concurrency is one of the few resources to focus on implementing algorithms in the shared-memory model of multi-core processors, rather than just theoretical models or distributed-memory architectures. The book provides detailed explanations and usable samples to help you transform algorithms from serial to parallel code, along with advice and analysis for avoiding mistakes that programmers typically...

"This book should find a place on any Propellerhead's bookshelf, between Parallax's Propeller Manual and its Programming and Customizing the Multicore Propeller volumes." Make: 24

Programming the Propeller with Spin: A Beginner's Guide to Parallel Processing walks you through the essential skills you need to build and control devices using the Propeller chip and its parallel processing environment. Find out how to use each of the identical 32-bit processors, known as cogs, and make the eight cogs effectively...

Programming multi-core and many-core computing systems

Sabri Pllana, Linnaeus University, Sweden

Fatos Xhafa, Technical University of Catalonia, Spain

Provides state-of-the-art methods for programming multi-core and many-core systems

The book comprises a selection of twenty two chapters covering: fundamental techniques and algorithms; programming approaches; methodologies and frameworks; scheduling and management; testing and evaluation methodologies; and case studies for programming multi-core and many-core systems.

Program...

It's tough to argue with R as a high-quality, cross-platform, open source statistical software product--unless you're in the business of crunching Big Data. This concise book introduces you to several strategies for using R to analyze large datasets, including three chapters on using R and Hadoop together. You'll learn the basics of Snow, Multicore, Parallel, Segue, RHIPE, and Hadoop Streaming, including how to find them, how to use them, when they work well, and when they don't.

With these packages, you can overcome R's single-threaded nature by spreading work across multiple CPUs,...

If you need to learn CUDA but don't have experience with parallel computing, CUDA Programming: A Developer's Introduction offers a detailed guide to CUDA with a grounding in parallel fundamentals. It starts by introducing CUDA and bringing you up to speed on GPU parallelism and hardware, then delving into CUDA installation. Chapters on core concepts including threads, blocks, grids, and memory focus on both parallel and CUDA-specific issues. Later, the book demonstrates CUDA in practice for optimizing applications, adjusting to new hardware, and solving common problems.

Your software needs to leverage multiple cores, handle thousands of users and terabytes of data, and continue working in the face of both hardware and software failure. Concurrency and parallelism are the keys, and Seven Concurrency Models in Seven Weeks equips you for this new world. See how emerging technologies such as actors and functional programming address issues with traditional threads and locks development. Learn how to exploit the parallelism in your computer's GPU and leverage clusters of machines with MapReduce and Stream Processing. And do it all with the confidence...