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Programming Massively Parallel Processors: A Hands-on Approach 3rd edition
345,04  Original price was: 345,04 €.172,52 Current price is: 172,52 €. su PVM Į krepšelį
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Programming Massively Parallel Processors: A Hands-on Approach 3rd edition

Original price was: 345,04 €.Current price is: 172,52 €. su PVM

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Programming Massively Parallel Processors: A Hands-on Approach shows both student and professional alike the basic concepts of parallel programming and GPU architecture. Various techniques for constructing parallel programs are explored in detail. Case studies demonstrate the development process, which begins with computational thinking and ends with effective and efficient parallel programs. Topics of performance, floating-point format, parallel patterns, and dynamic parallelism are covered in depth. For this new edition, the authors are updating their coverage of CUDA, including coverage of newer libraries such as CuDNN, moving content that has become less important to appendices, adding two new chapters on parallel patterns, and updating case studies to reflect current industry practices, while still retaining its concise, intuitive, practical approach based on years of road-testing in the authors’ own parallel computing courses.Teaches computational thinking and problem-solving techniques that facilitate high-performance parallel computingUtilizes CUDA version 7.5, NVIDIA’s software development tool created specifically for massively parallel environmentsContains new and updated case studiesIncludes coverage of newer libraries such as CuDNN for Deep Learning

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  • Overview of Programming Massively Parallel Processors: The book introduces fundamental concepts of parallel programming and GPU architecture, emphasizing hands-on learning for both students and professionals.

What are the main topics covered in ‘Programming Massively Parallel Processors: A Hands-on Approach’?

The book covers fundamental concepts of parallel programming and GPU architecture, including techniques for constructing parallel programs, performance issues, floating-point formats, parallel patterns, dynamic parallelism, and updates on CUDA and related libraries such as CuDNN.