Programming Massively Parallel Processors
Description
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. The new edition includes updated coverage of CUDA, including the newer libraries such as CuDNN. New chapters on frequently used parallel patterns have been added, and case studies have been updated to reflect current industry practices.
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Persons
is a Senior Director of
Research of NVIDIA and the
Sanders-AMD Endowed Chair
Professor Emeritus of Electrical
and Computer Engineering
at the University of Illinois
at Urbana-Champaign. His
work focuses on parallel
computing-covering
architecture, implementation,
compilers, and algorithms. Dr.
Hwu has received numerous
honors, including the ACM/
IEEE Eckert-Mauchly Award,
ACM Grace Murray Hopper
Award, IEEE B.R. Rau Award.
He is an IEEE and ACM
Fellow. He earned his Ph.D.
in Computer Science from UC
Berkele
Content
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: An introduction to constant memory and caching
8. Stencil
9. Parallel histogram
10. Reduction And minimizing divergence
11. Prefix sum (scan)
12. Merge: An introduction to dynamic input data identification
Part III Advanced patterns and applications
13. Sorting
14. Sparse matrix computation
15. Graph traversal
16 Deep learning
17. Iterative magnetic resonance imaging reconstruction
18. Electrostatic potential map
19. Parallel programming and computational thinking
Part IV Advanced Practices
20. Programming a heterogeneous computing cluster: An introduction to CUDA streams
21. CUDA dynamic parallelism
22. Advanced practices and future evolution
23. Conclusion and outlook
Appendix A: Numerical considerations