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Programming Massively Parallel Processors: A Hands-on Approach, Third Edition shows both student and professional alike the basic concepts of parallel programming and GPU architecture, exploring, in detail, various techniques for constructing parallel programs. Case studies demonstrate the development process, detailing computational thinking and ending 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 have updated their coverage of CUDA, including…mehr

Produktbeschreibung
Programming Massively Parallel Processors: A Hands-on Approach, Third Edition shows both student and professional alike the basic concepts of parallel programming and GPU architecture, exploring, in detail, various techniques for constructing parallel programs. Case studies demonstrate the development process, detailing computational thinking and ending 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 have updated their coverage of CUDA, including coverage of newer libraries, such as CuDNN, moved content that has become less important to appendices, added two new chapters on parallel patterns, and updated case studies to reflect current industry practices. Teaches computational thinking and problem-solving techniques that facilitate high-performance parallel computing Utilizes CUDA version 7.5, NVIDIA's software development tool created specifically for massively parallel environments Contains new and updated case studies Includes coverage of newer libraries, such as CuDNN for Deep Learning
  • Produktdetails
  • Verlag: Morgan Kaufmann
  • 3. Aufl.
  • Erscheinungstermin: 15. November 2016
  • Englisch
  • Abmessung: 231mm x 186mm x 32mm
  • Gewicht: 1167g
  • ISBN-13: 9780128119860
  • ISBN-10: 0128119861
  • Artikelnr.: 47207334
Inhaltsangabe
1. Introduction 2. Data parallel computing 3. Scalable parallel execution 4. Memory and data locality 5. Performance considerations 6. Numerical considerations 7. Parallel patterns: convolution: An introduction to stencil computation 8. Parallel patterns: prefix sum: An introduction to work efficiency in parallel algorithms 9. Parallel patterns-parallel histogram computation: An introduction to atomic operations and privatization 10. Parallel patterns: sparse matrix computation: An introduction to data compression and regularization 11. Parallel patterns: merge sort: An introduction to tiling with dynamic input data identification 12. Parallel patterns: graph search 13. CUDA dynamic parallelism 14. Application case study-non-Cartesian magnetic resonance imaging: An introduction to statistical estimation methods 15. Application case study-molecular visualization and analysis 16. Application case study-machine learning 17. Parallel programming and computational thinking 18. Programming a heterogeneous computing cluster 19. Parallel programming with OpenACC 20. More on CUDA and graphics processing unit computing 21. Conclusion and outlook