Programming Massively Parallel Processors, 4th edition

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Programming Massively Parallel Processors, 4th edition

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ISBN: 9780323912310
author: Wen-mei W. Hwu / David B. Kirk / Izzat El Hajj
publishing house: Morgan Kaufmann
publication date: 2022 -8
number of pages: 608

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A Hands-on Approach

Wen-mei W. Hwu / David B. Kirk …   

Overview

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.

contents

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

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