Programming Massively Parallel Processors: A Hands-on Approach,3rd Edition

Programming Massively Parallel Processors: A Hands-on Approach
Authors: David B. Kirk – Wen-mei W. Hwu
ISBN-10: 0128119861
ISBN-13: 9780128119860
Edition 版本:‏ 3
Released: 2016-12-21
Print Length 页数: 576 pages
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
Programming Massively Parallel Processors: A Hands-on
Approach
Copyright
Dedication
Preface
Acknowledgements
1Introduction
2 Data parallel computing
3 Scalable parallel execution
4 Memory and data locality
5 Performance considerations
6Numerical considerations
7 Parallel patterns: convolution
8 Parallel patterns: prefix sum
9 Parallel patterns-parallel histogram computation
10 Parallel patterns: sparse matrix computation
11 Parallel patterns: merge sort
12 Parallel patterns: graph search
13 CUDA dynamic parallelism
14 Application case study-non-Cartesian magnetic
resonance imaging
15 Application case study-molecular visualization and
analysis
16 Application case study-machine learning
17 Parallel programming and computational thinking
18Programming a heterogeneous computing cluster
19Parallel programming with OpenACC
20 More on CUDA and graphics processing unit computing
21Conclusion and outlook