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Programming Massively Parallel Processors: A Hands-on Approach

Autor Wen-Mei W. Hwu, David B. Kirk, Izzat El Hajj
en Limba Engleză Paperback – sep 2026
Programming Massively Parallel Processors: A Hands-on Approach 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 for the latest trends, incorporating the latest techniques and technologies to ensure that readers are learning the most current and relevant practices in GPU programming.

  • Introduces fundamental GPU programming concepts in a simple and approachable way, making it ideal for beginners, while gradually building up to more advanced topics
  • Beyond just teaching GPU programming basics, the book goes deeper by showing how to optimize parallelized code for real-world applications
  • Includes numerous worked examples that illustrate how to implement and optimize GPU programming patterns
  • Addresses the growing importance of deep learning and includes content on how GPUs are used in modern AI applications
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Specificații

ISBN-13: 9780443439001
ISBN-10: 0443439001
Pagini: 678
Dimensiuni: 191 x 235 mm
Ediția:5
Editura: ELSEVIER SCIENCE

Cuprins

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