Multicore And Gpu Programming An Integrated Approach Pdf

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Multicore and GPU Programming

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Multicore and GPU Programming offers broad coverage of the key parallel computing skillsets: multicore CPU programming and manycore "massively parallel" computing. Presenting material refined over more than a decade of teaching parallel computing, author Gerassimos Barlas minimizes the challenge with multiple examples, extensive case studies, and full source code. Using this book, you can develop programs that run over distributed memory machines using MPI, create multi-threaded applications with either libraries or directives, write optimized applications that balance the workload between available computing resources, and profile and debug programs targeting multicore machines. Graduate students in parallel computing courses covering both traditional and GPU computing or a two-semester sequence ; professionals and researchers looking to master parallel computing. His research interest includes parallel algorithms, development, analysis and modeling frameworks for load balancing, and distributed Video on-Demand.

Multicore And Gpu Programming

The new platforms demand a new approach to software development one that blends the tools and established practices of the network-of-workstations era with emerging software platforms such aS CUDA This book tries to address this need by covering the dominant contemporary tools and techniques, both in isolation and also most importantly in combination with each other. We strive to provide examples where multiple platforms and programming paradigms e. Introduction, designing multicore software: Chapter I introduces multicore hardware and examines influential instances of this architectural paradigm Chapter I also introduces speedup and efficiency, which are essential metrics used in the evaluation of multicore and parallel software. Amdahl's law and Gustafson-Barsis's rebuttal cap-up the chapter, providing estimates of what can XV Preface be expected from the exciting new developments in multicore and many-core hardware Chapter 2 is all about the methodology and the design patterns that can be employed in the development of parallel and multicore software. Both work decomposition patterns and program structure patterns are examined Shared-memory programming Two different approaches for shared-memory parallel programming are examined: explicit and implicit parallelization. On the explicit side, Chapter 3 covers threads and two of the most commonly used synchronization mechanisms, semaphores and monitors. Frequently encountered design patterns, such as producers-consumers and readers-writers are explained thoroughly and applied in a range of examples On the implicit side, Chapter 4 covers the OpenMP standard that has been specifically designed for parallelizing existing sequential code with minimum effort Development time is significantly reduced as a result.

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Full E-book Multicore and Gpu Programming: An Integrated Approach Review

Preface Parallel computing has been given a fresh breath of life since the emergence of multicore architectures in the first decade of the new century. The new platforms demand a new approach to software development; one that blends the tools and established practices of the network-of-workstations era with emerging software platforms such as CUDA. This book tries to address this need by covering the dominant contemporary tools and techniques, both in isolation and also most importantly in combination with each other.

E-raamatut saab lugeda ning alla laadida kuni 6'de seadmesse. E-raamatut ei saa konverteerida teise formaati ega lugeda Amazon Kindle's. Multicore and GPU Programming offers broad coverage of the key parallel computing skillsets: multicore CPU programming and manycore "massively parallel" computing.

Multicore systems support MultiThreading and Parallel Computing. Multicore processors are widely used across many application domains, including general-purpose, embedded, network, digital signal processing DSP , and graphics GPU. Efficient software algorithms should be used for implementation of cores to achieve higher performance. Multicore and GPU Programming offers broad coverage of the key parallel computing skillsets: multicore CPU programming and manycore massively parallel computing. Continual exponential speed increases resulting from the realization of Moore's Law has come to an end, at least.

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