Programming massively parallel processors hands-on with CUDA / by David Kirk and Wen-mei Hwu.
Material type:
TextPublication details: Burlington, MA : Morgan Kaufmann Publishers, c2010.Description: xviii, 258 p. : ill. ; 24 cmISBN: - 9780123814722
- 004.35 22
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Main library General Stacks | 004.35 / KI.P 2010 (Browse shelf(Opens below)) | 1 | Available | 011511 |
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| 004.35 / BH.P 2009 Parallel computing / | 004.35 / CU.P 1999 Parallel computer architecture : | 004.35 / DO.S 2008 Software development for embedded multi-core systems : | 004.35 / KI.P 2010 Programming massively parallel processors hands-on with CUDA / | 004.35 / TA.S 2006 Synchronization algorithms and concurrent programming / | 004.36 / BA.M 2010 Management strategies for the cloud revolution : | 004.36 / BR.P 2008 Practical distributed processing / |
Includes bibliographical references and index.
1. Introduction -- 2. History of GPU Computing -- 3. Introduction to CUDA -- 4. CUDA Threads -- 5. CUDA Memories -- 6. Performance Considerations -- 7. Floating-Point Format -- 8. Application Case Study I - Advanced MRI Reconstruction -- 9. Application Case Study II - Molecular Visualization and Analysis -- 10. Parallel Programming and Computational Thinking -- 11. A Brief Introduction to OpenCL -- 12. Conclusion and Future Outlook.
Multi-core processors are no longer the future of computing-they are the present day reality. A typical mass-produced CPU features multiple processor cores, while a GPU (Graphics Processing Unit) may have hundreds or even thousands of cores. With the rise of multi-core architectures has come the need to teach advanced programmers a new and essential skill: how to program massively parallel processors. 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.
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