Programming Massively Parallel Processors: A Hands-on Approach shows both student and professional alike the basic concepts of parallel programming and GPU architecture. Various techniques for constructing parallel programs are explored in detail. Case studies demonstrate the development process, which begins with computational thinking and ends with effective and efficient parallel programs. Topics of performance, floating-point format, parallel patterns, and dynamic parallelism are covered in depth. For this new edition, the authors are updating their coverage of CUDA, including the concept…mehr
Programming Massively Parallel Processors: A Hands-on Approach shows both student and professional alike the basic concepts of parallel programming and GPU architecture. Various techniques for constructing parallel programs are explored in detail. Case studies demonstrate the development process, which begins with computational thinking and ends with effective and efficient parallel programs. Topics of performance, floating-point format, parallel patterns, and dynamic parallelism are covered in depth. For this new edition, the authors are updating their coverage of CUDA, including the concept of unified memory, and expanding content in areas such as threads, while still retaining its concise, intuitive, practical approach based on years of road-testing in the authors' own parallel computing courses.--Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
Wen-mei W. Hwu is a Professor and holds the Sanders-AMD Endowed Chair in the Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign. His research interests are in the area of architecture, implementation, compilation, and algorithms for parallel computing. He is the chief scientist of Parallel Computing Institute and director of the IMPACT research group (www.impact.crhc.illinois.edu). He is a co-founder and CTO of MulticoreWare. For his contributions in research and teaching, he received the ACM SigArch Maurice Wilkes Award, the ACM Grace Murray Hopper Award, the Tau Beta Pi Daniel C. Drucker Eminent Faculty Award, the ISCA Influential Paper Award, the IEEE Computer Society B. R. Rau Award and the Distinguished Alumni Award in Computer Science of the University of California, Berkeley. He is a fellow of IEEE and ACM. He directs the UIUC CUDA Center of Excellence and serves as one of the principal investigators of the NSF Blue Waters Petascale computer project. Dr. Hwu received his Ph.D. degree in Computer Science from the University of California, Berkeley.
Inhaltsangabe
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
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
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