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  • Programming Massively Parallel Processors: A Hands-on Approach

    Programming Massively Parallel Processors by Hwu, Wen-mei W.; Kirk, David B.; El Hajj, Izzat;

    A Hands-on Approach

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      • Publisher's listprice EUR 82.99
      • The price is estimated because at the time of ordering we do not know what conversion rates will apply to HUF / product currency when the book arrives. In case HUF is weaker, the price increases slightly, in case HUF is stronger, the price goes lower slightly.

        32 415 Ft (30 872 Ft + 5% VAT)
      • Discount 10% (cc. 3 242 Ft off)
      • Discounted price 29 174 Ft (27 785 Ft + 5% VAT)

    32 415 Ft

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    Product details:

    • Edition number 5
    • Publisher Elsevier Science
    • Date of Publication 19 June 2026

    • ISBN 9780443439001
    • Binding Paperback
    • No. of pages680 pages
    • Size 235x191 mm
    • Weight 450 g
    • Language English
    • 700

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    Long description:

    Programming Massively Parallel Processors: A Hands-on Approach, Fifth Edition shows both students and professionals alike the basic concepts of parallel programming and GPU architecture. Concise, intuitive, and practical, it is based on years of road-testing in the authors' own parallel computing courses. Various techniques for constructing and optimizing parallel programs are explored in detail, while case studies demonstrate the development process, which begins with computational thinking and ends with effective and efficient parallel programs. This new edition has been updated with an expanded repertoire of optimizations, new patterns and applications, ad more coverage of important CUDA features.

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    Table of Contents:

    1. Introduction

    Part I. Fundamental Concepts
    2. Heterogeneous data parallel computing
    3. Multidimensional grids and data
    4. Compute architecture and scheduling
    5. Memory architecture and data locality
    6. Performance considerations

    Part II. Parallel Patterns
    7. Convolution
    8. Stencil
    9. Parallel histogram
    10. Reduction
    11. Prefix sum (scan)
    12. Merge

    Part III. Advanced Patterns and Applications
    13. Sorting
    14. Filtering (new)
    15. Sparse matrix computation
    16. Wavefront Algorithms (new)
    17. Graph traversal
    18. Deep learning
    19. Multi-GPU API (new)
    20. Electrostatic potential map
    21. Parallel programming and computational thinking

    Part IV. Advanced Practices
    22. Programming a heterogeneous computing cluster
    23. Advanced Optimizations for Matrix Multiplication (new)
    24. Advanced practices and future evolution
    25. Conclusion and outlook

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