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Kirk D.B., Hwu W.W. Programming Massively Parallel Processors: A Hands-on Approach

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Kirk D.B., Hwu W.W. Programming Massively Parallel Processors: A Hands-on Approach
2nd Edition. — Morgan Kaufmann, 2012 — 514 p. — ISBN10: 0124159923, ISBN13: 978-0124159921.
This best-selling guide to CUDA and GPU parallel programming has been revised with more parallel programming examples, commonly-used libraries, and explanations of the latest tools. With these improvements, the book retains its concise, intuitive, practical approach based on years of road-testing in the authors' own parallel computing courses.
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.
Updates in this edition include:
New coverage of CUDA 5.0, improved performance, enhanced development tools, increased hardware support, and more.
Increased coverage of related technology OpenCL and new material on algorithm patterns, GPU clusters, host programming, and data parallelism.
Two new case studies explore the latest applications of CUDA and GPUs for scientific research and high-performance computing.
History of GPU Computing.
Introduction to Data Parallelism and CUDA C.
Data-Parallel Execution Model.
CUDA Memories.
Performance Considerations.
Floating-Point Considerations.
Parallel Patterns: Convolutions.
Parallel Patterns: Prefix Sum.
Parallel Patterns: Sparse Matrix-Vector Multiplication.
Application Case Study: Advanced MRI Reconstruction.
Application Case Study: Molecular Visualization and Analysis.
Parallel Programming and Computational Thinking.
An Introduction to OpenCL.
Parallel Programming with OpenACC.
Thrust: A Productivity-Oriented Library for CUDA.
CUDA FORTRAN.
An Introduction to C++ AMP.
Programming a Heterogeneous Computing Cluster.
CUDA Dynamic Parallelism.
Conclusions and Future Outlook.
Appendix A: Matrix Multiplication Host-Only Version Source Code.
Appendix B: GPU Compute Capabilities.
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