Multicore and GPU Programming

This book PDF is perfect for those who love Computers genre, written by Gerassimos Barlas and published by Morgan Kaufmann which was released on 15 December 2021 with total hardcover pages 736. You could read this book directly on your devices with pdf, epub and kindle format, check detail and related Multicore and GPU Programming books below.

Multicore and GPU Programming
Author : Gerassimos Barlas
File Size : 44,7 Mb
Publisher : Morgan Kaufmann
Language : English
Release Date : 15 December 2021
ISBN : 0128141204
Pages : 736 pages
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Multicore and GPU Programming by Gerassimos Barlas Book PDF Summary

Multicore and GPU Programming: An Integrated Approach offers broad coverage of the key parallel computing skillsets: multicore CPU programming and manycore "massively parallel" computing. Using threads, OpenMP, MPI, CUDA, and other current tools it teaches the design and development of software capable of taking advantage of today's computing platforms incorporating CPU and GPU hardware and explains how to transition from sequential programming to a parallel computing paradigm. 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, readers 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. Comprehensive coverage of all major multicore programming tools, including threads, OpenMP, MPI, and CUDA, with coverage of OpenCL and OpenACC added Demonstrates parallel programming design patterns and examples of how different tools and paradigms can be integrated for superior performance New features in the second edition include the use of the C++14 standard for all sample code, a new chapter on concurrent data structures, and the latest research on load balancing Download source code, examples, and instructor support materials on the book's companion website

Multicore and GPU Programming

Multicore and GPU Programming: An Integrated Approach offers broad coverage of the key parallel computing skillsets: multicore CPU programming and manycore "massively parallel" computing. Using threads, OpenMP, MPI, CUDA, and other current tools it teaches the design and development of software capable of taking advantage of today's computing platforms incorporating

DOWNLOAD
Multicore and GPU Programming

Multicore and GPU Programming offers broad coverage of the key parallel computing skillsets: multicore CPU programming and manycore "massively parallel" computing. Using threads, OpenMP, MPI, and CUDA, it teaches the design and development of software capable of taking advantage of today’s computing platforms incorporating CPU and GPU hardware and

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Multicore and GPU Programming

Multicore and GPU Programming: An Integrated Approach, Second Edition offers broad coverage of key parallel computing tools, essential for multi-core CPU programming and many-core "massively parallel" computing. Using threads, OpenMP, MPI, CUDA and other state-of-the-art tools, the book teaches the design and development of software capable of taking advantage of

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GPU Programming in MATLAB

GPU programming in MATLAB is intended for scientists, engineers, or students who develop or maintain applications in MATLAB and would like to accelerate their codes using GPU programming without losing the many benefits of MATLAB. The book starts with coverage of the Parallel Computing Toolbox and other MATLAB toolboxes for

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Parallel Programming with OpenACC

Parallel Programming with OpenACC is a modern, practical guide to implementing dependable computing systems. The book explains how anyone can use OpenACC to quickly ramp-up application performance using high-level code directives called pragmas. The OpenACC directive-based programming model is designed to provide a simple, yet powerful, approach to accelerators without

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Parallel Programming

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

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CUDA by Example

CUDA is a computing architecture designed to facilitate the development of parallel programs. In conjunction with a comprehensive software platform, the CUDA Architecture enables programmers to draw on the immense power of graphics processing units (GPUs) when building high-performance applications. GPUs, of course, have long been available for demanding graphics

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Parallel and Concurrent Programming in Haskell

If you have a working knowledge of Haskell, this hands-on book shows you how to use the language’s many APIs and frameworks for writing both parallel and concurrent programs. You’ll learn how parallelism exploits multicore processors to speed up computation-heavy programs, and how concurrency enables you to write

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