大规模并行处理器程序设计（英文版·原书第3版） mobi 下载 网盘 caj lrf pdf txt 阿里云

本书介绍并行编程和GPU架构的基本概念，详细探索了构建并行程序的各种技术，涵盖性能、浮点格式、并行模式和动态并行等主题，适合专业人士及学生阅读。书中通过案例研究展示了开发过程，从计算思维的细节着手，最终给出了高效的并行程序示例。新版更新了关于CUDA的讨论，包含CuDNN等新的库，同时将不再重要的内容移到附录中。新版还增加了关于并行模式的两个新章节，并更新了案例研究，以反映当前的行业实践。

Preface Acknowledgements

CHAPTER.1 Introduction........................................1

1.1 Heterogeneous Parallel Computing........................................2

1.2 Architecture of a Modern GPU........................................6

1.3 Why More Speed or Parallelism........................................8

1.4 Speeding Up Real Applications........................................10

1.5 Challenges in Parallel Programming ........................................12

1.6 Parallel Programming Languages and Models.............................12

1.7 Overarching Goals........................................14

1.8 Organization of the Book........................................15

References ........................................18

CHAPTER.2 Data Parallel Computing........................................19

2.1 Data Parallelism........................................20

2.2 CUDA C Program Structure........................................22

2.3 A Vector Addition Kernel ........................................25

2.4 Device Global Memory and Data Transfer...................................27

2.5 Kernel Functions and Threading........................................32

2.6 Kernel Launch........................................37

2.7 Summary........................................38

Function Declarations........................................38

Kernel Launch........................................38

Built-in (Predefined) Variables ........................................39

Run-time API........................................39

2.8 Exercises........................................39

References ........................................41

CHAPTER.3 Scalable Parallel Execution........................................43

3.1 CUDA Thread Organization........................................43

3.2 Mapping Threads to Multidimensional Data................................47

3.3 Image Blur: A More Complex Kernel ........................................54

3.4 Synchronization and Transparent Scalability ...............................58

3.5 Resource Assignment........................................60

3.6 Querying Device Properties........................................61

3.7 Thread Scheduling and Latency Tolerance...................................64

3.8 Summary........................................67

3.9 Exercises........................................67

CHAPTER.4 Memory and Data Locality ........................................71

4.1 Importance of Memory Access Efficiency....................................72

4.2 Matrix Multiplication........................................73

4.3 CUDA Memory Types........................................77

4.4 Tiling for Reduced Memory Traffic........................................84

4.5 A Tiled Matrix Multiplication Kernel........................................90

4.6 Boundary Checks........................................94

4.7 Memory as a Limiting Factor to Parallelism................................97

4.8 Summary........................................99

4.9 Exercises........................................100

CHAPTER.5 Performance Considerations........................................103

5.1 Global Memory Bandwidth........................................104

5.2 More on Memory Parallelism........................................112

5.3 Warps and SIMD Hardware........................................117

5.4 Dynamic Partitioning of Resources........................................125

5.5 Thread Granularity........................................127

5.6 Summary........................................128

5.7 Exercises........................................128

References ........................................130

CHAPTER.6 Numerical Considerations ........................................131

6.1 Floating-Point Data Representation........................................132

Normalized Representation of M........................................132

Excess Encoding of E ........................................133

6.2 Representable Numbers........................................134

6.3 Special Bit Patterns and Precision in IEEE Format....................138

6.4 Arithmetic Accuracy and Rounding ........................................139

6.5 Algorithm Considerations........................................140

6.6 Linear Solvers and Numerical Stability......................................142

6.7 Summary........................................146

6.8 Exercises........................................147

References ........................................147

CHAPTER.7 Parallel Patterns: Convolution ........................................149

7.1 Background........................................150

7.2 1D Parallel Convolution—A Basic Algorithm ...........................153

7.3 Constant Memory and Caching........................................156

7.4 Tiled 1D Convolution with Halo Cells.......................................160

7.5 A Simpler Tiled 1D Convolution—General Caching.................165

7.6 Tiled 2D Convolution with Halo Cells.......................................166

7.7 Summary........................................172

7.8 Exercises........................................173

CHAPTER.8 Parallel Patterns: Prefix Sum........................................175

8.1 Background........................................176

8.2 A Simple Parallel Scan........................................177

8.3 Speed and Work Efficiency........................................181

8.4 A More Work-Efficient Parallel Scan........................................183

8.5 An Even More Work-Efficient Parallel Scan..............................187

8.6 Hierarchical Parallel Scan for Arbitrary-Length Inputs..............189

8.7 Single-Pass Scan for Memory Access Efficiency.......................192

8.8 Summary........................................195

8.9 Exercises........................................195

References ........................................196

CHAPTER.9 Parallel Patterns Parallel Histogram Computation .. 199

9.1 Background........................................200

9.2 Use of Atomic Operations ........................................202

9.3 Block versus Interleaved Partitioning........................................206

9.4 Latency versus Throughput of Atomic Operations.....................207

9.5 Atomic Operation in Cache Memory ........................................210

9.6 Privatization........................................210

9.7 Aggregation ........................................211

9.8 Summary........................................213

9.9 Exercises........................................213

Reference........................................214

CHAPTER.10 Parallel Patterns: Sparse Matrix Computation ...........215

10.1 Background........................................216

10.2 Parallel SpMV Using CSR........................................219

10.3 Padding and Transposition........................................221

10.4 Using a Hybrid Approach to Regulate Padding.......................224

10.5 Sorting and Partitioning for Regularization.............................227

10.6 Summary........................................229

10.7 Exercises........................................229

References ........................................230

CHAPTER.11 Parallel Patterns: Merge Sort........................................231

11.1 Background........................................231

11.2 A Sequential Merge Algorithm........................................233

11.3 A Parallelization Approach........................................234

11.4 Co-Rank Function Implementation........................................236

Contents

11.5 A Basic Parallel Merge Kernel ........................................241

11.6 A Tiled Merge Kernel........................................242

11.7 A Circular-Buffer Merge Kernel........................................249

11.8 Summary........................................256

11.9 Exercises........................................256

Reference........................................256

CHAPTER.12 Parallel Patterns: Graph Search......................................257

12.1 Background........................................258

12.2 Breadth-First Search ........................................260

12.3 A Sequential BFS Function ........................................262

12.4 A Parallel BFS Function........................................265

12.5 Optimizations........................................270

Memory Bandwidth........................................270

Hierarchical Queues ........................................271

Kernel Launch Overhead........................................272

Load Balance........................................273

12.6 Summary........................................273

12.7 Exercises........................................273

References ........................................274

CHAPTER.13 CUDA Dynamic Parallelism........................................275

13.1 Background........................................276

13.2 Dynamic Parallelism Overview ........................................278

13.3 A Simple Example........................................279

13.4 Memory Data Visibility........................................281

Global Memory ........................................281

Zero-Copy Memory........................................282

Constant Memory........................................282

Local Memory........................................282

Shared Memory........................................283

Texture Memory........................................283

13.5 Configurations and Memory Management ..............................283

Launch Environment Configuration........................................283

Memory Allocation and Lifetime........................................283

Nesting Depth........................................284

Pending Launch Pool Configuration .......................................284

Errors and Launch Failures........................................284

13.6 Synchronization, Streams, and Events.....................................285

Synchronization........................................285

Synchronization Depth........................................285

Streams ........................................286

Events ........................................287

13.7 A More Complex Example........................................287

Linear Bezier Curves........................................288

Quadratic Bezier Curves........................................288

Bezier Curve Calculation (Without Dynamic Parallelism) .....288

Bezier Curve Calculation (With Dynamic Parallelism) ..........290

Launch Pool Size........................................292

Streams ........................................292

13.8 A Recursive Example........................................293

13.9 Summary........................................297

13.10 Exercises........................................299

References ........................................301

A13.1 Code Appendix........................................301

CHAPTER.14 Application Case Study—non-Cartesian Magnetic Resonance Imaging............................305

14.1 Background........................................306

14.2 Iterative Reconstruction........................................308

14.3 Computing FHD ........................................310

Step 1: Determine the Kernel Parallelism Structure................312

Step 2: Getting Around the Memory Bandwidth Limitation...317

Step 3: Using Hardware Trigonometry Functions...........323

Step 4: Experimental Performance Tuning..............................326

14.4 Final Evaluation........................................327

14.5 Exercises........................................328

References ........................................329

CHAPTER.15 Application Case Study—Molecular Visualization and Analysis ....................................331

15.1 Background........................................332

15.2 A Simple Kernel Implementation........................................333

15.3 Thread Granularity Adjustment ........................................337

15.4 Memory Coalescing........................................338

15.5 Summary........................................342

15.6 Exercises........................................343

References ........................................344

CHAPTER.16 Application Case Study—Machine Learning ..............345

16.1 Background........................................346

16.2 Convolutional Neural Networks ........................................347

ConvNets: Basic Layers........................................348

ConvNets: Backpropagation........................................351

16.3 Convolutional Layer: A Basic CUDA Implementation of Forward Propagation.............................355

16.4 Reduction of Convolutional Layer to Matrix Multiplication........................................359

16.5 cuDNN Library........................................364

16.6 Exercises........................................366

References ........................................367

CHAPTER.17 Parallel Programming and Computational Thinking ........................................369

17.1 Goals of Parallel Computing........................................370

17.2 Problem Decomposition........................................371

17.3 Algorithm Selection........................................374

17.4 Computational Thinking........................................379

17.5 Single Program, Multiple Data, Shared Memory and Locality ...................................380

17.6 Strategies for Computational Thinking....................................382

17.7 A Hypothetical Example: Sodium Map of the Brain...............383

17.8 Summary........................................386

17.9 Exercises........................................386

References ........................................386

CHAPTER.18 Programming a Heterogeneous Computing Cluster ........................................387

18.1 Background........................................388

18.2 A Running Example........................................388

18.3 Message Passing Interface Basics........................................391

18.4 Message Passing Interface Point-to-Point Communication.....393

18.5 Overlapping Computation and Communication......................400

18.6 Message Passing Interface Collective Communication...........408

18.7 CUDA-Aware Message Passing Interface ...............................409

18.8 Summary........................................410

18.9 Exercises........................................410

Reference........................................411

CHAPTER.19 Parallel Programming with OpenACC.............................413

19.1 The OpenACC Execution Model........................................414

19.2 OpenACC Directive Format........................................416

19.3 OpenACC by Example........................................418

The OpenACC Kernels Directive........................................419

The OpenACC Parallel Directive ........................................422

Comparison of Kernels and Parallel Directives.......................424

OpenACC Data Directives........................................425

OpenACC Loop Optimizations........................................430

OpenACC Routine Directive........................................432

Asynchronous Computation and Data.....................................434

19.4 Comparing OpenACC and CUDA........................................435

Portability ........................................435

Performance........................................436

Simplicity ........................................436

19.5 Interoperability with CUDA and Libraries..............................437

Calling CUDA or Libraries with OpenACC Arrays................437

Using CUDA Pointers in OpenACC .......................................438

Calling CUDA Device Kernels from OpenACC.....................439

19.6 The Future of OpenACC........................................440

19.7 Exercises........................................441

CHAPTER.20 More on CUDA and Graphics Processing Unit Computing........................................443

20.1 Model of Host/Device Interaction........................................444

20.2 Kernel Execution Control ........................................449

20.3 Memory Bandwidth and Compute Throughput.......................451

20.4 Programming Environment........................................453

20.5 Future Outlook........................................455

References ........................................456

CHAPTER.21 Conclusion and Outlook........................................457

21.1 Goals Revisited........................................457

21.2 Future Outlook........................................458

Appendix A: An Introduction to OpenCL........................................461

Appendix B: THRUST: a Productivity-oriented Library for CUDA.....................475

Appendix C: CUDA Fortran........................................493

Appendix D: An introduction to C++ AMP........................................515

Index ........................................535

[美]大卫·B. 柯克（David B. Kirk） 胡文美（Wen-mei W. Hwu） 著：大卫·B. 柯克（David B. Kirk） 美国国家工程院院士，NVIDIA Fellow，曾任NVIDIA公司首席科学家。他领导了NVIDIA图形技术的开发，并且是CUDA技术的创始人之一。2002年，他荣获ACM SIGGRAPH计算机图形成就奖，以表彰其在把高性能计算机图形系统推向大众市场方面做出的杰出贡献。他拥有加州理工学院计算机科学博士学位。

胡文美（Wen-mei W. Hwu） 美国伊利诺伊大学厄巴纳-香槟分校电气与计算机工程系AMD Jerry Sanders讲席教授，并行计算研究中心首席科学家，领导IMPACT团队和CUDA卓越中心的研究工作。他在编译器设计、计算机体系结构、微体系结构和并行计算方面做出了卓越贡献，是IEEE Fellow、ACM Fellow，荣获了包括ACM SigArch Maurice Wilkes Award在内的众多奖项。他还是MulticoreWare公司的联合创始人兼CTO。他拥有加州大学伯克利分校计算机科学博士学位。

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书籍介绍

本书介绍并行编程和GPU架构的基本概念，详细探索了构建并行程序的各种技术，涵盖性能、浮点格式、并行模式和动态并行等主题，适合专业人士及学生阅读。书中通过案例研究展示了开发过程，从计算思维的细节着手，最终给出了高效的并行程序示例。新版更新了关于CUDA的讨论，包含CuDNN等新的库，同时将不再重要的内容移到附录中。新版还增加了关于并行模式的两个新章节，并更新了案例研究，以反映当前的行业实践。