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大規(guī)模并行處理器程序設(shè)計（英文版·原書第3版） 
定 價：139 元 叢書名：經(jīng)典原版書庫
9 7 6 8 6 7 8 1 3 1 6 1 7 
本書介紹并行編程和GPU架構(gòu)的基本概念，詳細探索了構(gòu)建并行程序的各種技術(shù)，涵蓋性能、浮點格式、并行模式和動態(tài)并行等主題，適合專業(yè)人士及學生閱讀。書中通過案例研究展示了開發(fā)過程，從計算思維的細節(jié)著手，*終給出了高效的并行程序示例。新版更新了關(guān)于CUDA的討論，包含CuDNN等新的庫，同時將不再重要的內(nèi)容移到附錄中。新版還增加了關(guān)于并行模式的兩個新章節(jié)，并更新了案例研究，以反映當前的行業(yè)實踐。
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
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