Programming Massively Parallel Processors: A Hands-on Approach
Autor Wen-Mei W. Hwu, David B. Kirk, Izzat El Hajjen Limba Engleză Paperback – sep 2026
- 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
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
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