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基本説明
旧版邦訳：ボーンデジタル・2010年。
This best-selling guide to CUDA and GPU parallel programming has been revised with more parallel examples, commonly-used libraries, and explanations of the latest tools.
Full Description
Programming Massively Parallel ProcessorsEdition, teaches students how to program massively parallel processors. It offers a detailed discussion of various techniques for constructing parallel programs. Case studies are used to demonstrate the development process, which begins with computational thinking and ends with effective and efficient parallel programs.This guide shows both student and professional alike the basic concepts of parallel programming and GPU architecture. Topics of performance, floating-point format, parallel patterns, and dynamic parallelism are covered in depth. This revised edition contains more parallel programming examples, commonly-used libraries such as Thrust, and explanations of the latest tools. It also provides new coverage of CUDA 5.0, improved performance, enhanced development tools, increased hardware support, and more; increased coverage of related technology, OpenCL and new material on algorithm patterns, GPU clusters, host programming, and data parallelism; and two new case studies (on MRI reconstruction and molecular visualization) that explore the latest applications of CUDA and GPUs for scientific research and high-performance computing.This book should be a valuable resource for advanced students, software engineers, programmers, and hardware engineers.
Contents
1 Introduction 2 History of GPU Computing 3 Introduction to Data Parallelism and CUDA C 4 Data-Parallel Execution Model 5 CUDA Memories 6 Performance Considerations 7 Floating-Point Considerations 8 Parallel Patterns: Convolutions 9 Parallel Patterns: Prefix Sum 10 Parallel Patterns: Sparse Matrix-Vector Multiplication 11 Application Case Study: Advanced MRI Reconstruction 12 Application Case Study: Molecular Visualization and Analysis 13 Parallel Programming and Computational Thinking 14 An Introduction to OpenCL 15 Parallel Programming with OpenACC 16 Thrust: A Productivity-Oriented Library for CUDA 17 CUDA FORTRAN 18 An Introduction to C++ AMP 19 Programming a Heterogeneous Computing Cluster 20 CUDA Dynamic Parallelism 21 Conclusions and Future OutlookAppendix A: Matrix Multiplication Host-Only Version Source Code Appendix B: GPU Compute Capabilities
