Programming Massively Parallel Processors: A Hands-on Approach

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. Teaches computational thinking and problem-solving techniques that facilitate high-performance parallel computing Updated to utilize CUDA version 10.0, NVIDIA's software development tool created specifically for massively parallel environments Features new content on unified memory, as well as expanded content on threads, streams, warp divergence, and OpenMP Includes updated and new case studies INDICE: 1. Introduction2. Data parallel computing3. Scalable parallel execution4. Memory and data locality5. Performance considerations6. Numerical considerations7. Parallel patterns: convolution: An introduction to stencil computation8. Parallel patterns: prefix sum: An introduction to work efficiency in parallel algorithms9. Parallel patterns-parallel histogram computation: An introduction to atomic operations and privatization10. Parallel patterns: sparse matrix computation: An introduction to data compression and regularization11. Parallel patterns: merge sort: An introduction to tiling with dynamic input data identification12. Parallel patterns: graph search13. CUDA dynamic parallelism14. Application case study-non-Cartesian magnetic resonance imaging: An introduction to statistical estimation methods15. Application case study-molecular visualization and analysis16. Application case study-machine learning17. Parallel programming and computational thinking18. Programming a heterogeneous computing cluster19. Parallel programming with OpenACC20. More on CUDA and graphics processing unit computing21. Conclusion and outlook

• ISBN: 978-0-323-91231-0
• Editorial: Morgan Kaufmann
• Encuadernacion: Rústica
• Páginas: 580
• Fecha Publicación: 01/08/2022
• Nº Volúmenes: 1
• Idioma: Inglés