Programming Massively Parallel Processors

A Hands-on Approach
4. Auflage 2022
ISBN: 978-0-323-91231-0
Verlag: Elsevier Science & Technology

Buch, Englisch, 580 Seiten, Format (B × H): 187 mm x 233 mm, Gewicht: 916 g

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.

Autoren/Hrsg.

Hwu, Wen-mei W.

Kirk, David B.

El Hajj, Izzat

Fachgebiete

• Mathematik | Informatik EDV | Informatik Technische Informatik Grid-Computing & Paralleles Rechnen
• Mathematik | Informatik EDV | Informatik Programmierung | Softwareentwicklung Funktionale, Logische, Parallele und Visuelle Programmierung

Inhaltsverzeichnis

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: 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

Über Autor(innen)

El Hajj, Izzat
Izzat El Hajj
is an Assistant Professor
of Computer Science at
the American University
of Beirut. His research
focuses on leveraging
accelerator architectures
to tackle challenging
computations, with a
focus on GPU computing,
processing-in-memory,
and performance
modeling. He earned his
Ph.D. in Electrical and
Computer Engineering at
the University of Illinois
at Urbana-Champaign.
He has received the
Dan Vivoli Endowed
Fellowship (UIUC) and the
Distinguished Graduate
Award from the American
University of Beirut.

Kirk, David B.
David B. Kirk
is known for major
contributions to graphics,
hardware, and algorithms.
Before pursuing his Ph.D. at
Caltech, he earned B.S. and
M.S. degrees in mechanical
engineering from MIT and
worked at Raster Technologies
and Hewlett-Packard’s Apollo
Systems Division. After
completing his doctorate, he
served as chief scientist and
head of technology at Crystal
Dynamics. In 1997, he became
Chief Scientist at NVIDIA. Dr.
Kirk has received numerous
honors including the IEEE
Seymour Cray Computer
Engineering Award and
ACM SIGGRAPH Computer
Graphics Achievement
Award. He is a member of
the U.S. National Academy of
Engineering.

Hwu, Wen-mei W.
Wen-mei W. Hwu
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