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The CUDA handbook: a comprehensive guide to GPU programming

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Bibliographic Details
Main Author:	Wilt, Nicholas 1970- (Author)
Format:	Book
Language:	English
Published:	Upper Saddle River, NJ ; Munich [u.a.] Addison-Wesley 2013
Edition:	1. printing
Subjects:	Grafikprozessor Programmierung Parallelverarbeitung CUDA > Informatik Application software > Development. Computer architecture. Graphics processing units > Programming.
Online Access:	Inhaltsverzeichnis
Physical Description:	XXV, 494 S. graph. Darst.
ISBN:	9780321809469 0321809467

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adam_text	Contents Preface .....................................xxi Acknowledgments ..............................xxiii About the Author .............................. xxv PARTI _______________________________________ Ì Chapter 1: Background .......................3 1.1 Our Approach ..................................5 1.2 Code ........................................6 1.2.1 Microbenchmarks ............................6 1.2.2 Microdemos................................7 1.2.3 Optimization Journeys ..........................7 1.3 Administrative Items ..............................7 1.3.1 Open Source ................................7 1.3.2 CUDA Handbook Library (chLib) ....................8 1.3.3 Coding Style ................................8 1.3.4 CUDA SDK .................................8 1.4 Road Map .....................................8 Chapter 2: Hardware Architecture ............... 11 2.1 CPU Configurations ..............................11 2.1.1 Front-Side Bus .............................12 CONTENTS 2.1.2 Symmetric Multiprocessors ......................13 2.1.3 Nonuniform Memory Access .....................14 2.1.4 PCI Express Integration ........................17 2.2 Integrated GPUs ................................17 2.3 Multiple GPUs .................................19 2.4 Address Spaces in CUDA ...........................22 2.4.1 Virtual Addressing: A Brief History ..................22 2.4.2 Disjoint Address Spaces ........................26 2.4.3 Mapped Pinned Memory ........................28 2.4.4 Portable Pinned Memory .......................29 2.4.5 Unified Addressing ...........................30 2.4.6 Peer-to-Peer Mappings ........................31 2.5 CPU/GPU Interactions ............................32 2.5.1 Pinned Host Memory and Command Buffers ............32 2.5.2 CPU/GPU Concurrency ........................35 2.5.3 The Host Interface and Intra-GPU Synchronization ........39 2.5.4 Inter-GPU Synchronization ......................41 2.6 GPU Architecture ...............................41 2.6.1 Overview .................................42 2.6.2 Streaming Multiprocessors .....................46 2.7 Further Reading ................................50 Chapters·. Software Architecture ................51 3.1 Software Layers ................................51 3.1.1 CUDA Runtime and Driver .......................53 3.1.2 Driver Models ..............................54 3.1.3 nvcc, PTX, and Microcode ........... 57 CONTENTS 3.2 Devices and Initialization ...........................59 3.2.1 Device Count ............................... 60 3.2.2 Device Attributes ............................ 60 3.2.3 When CUDA Is Not Present ...................... 63 3.3 Contexts .................................... 67 3.3.1 Lifetime and Scoping .......................... 68 3.3.2 Preallocation of Resources ...................... 68 3.3.3 Address Space ............................. 69 3.3.4 Current Context Stack ......................... 69 3.3.5 Context State .............................. 71 3.4 Modules and Functions ............................ 71 3.5 Kernels (Functions) .............................. 73 3.6 Device Memory ................................ 75 3.7 Streams and Events .............................. 76 3.7.1 Software Pipelining ........................... 76 3.7.2 Stream Callbacks ............................ 77 3.7.3 The NULL Stream ............................ 77 3.7.4 Events .................................. 78 3.8 Host Memory .................................. 79 3.8.1 Pinned Host Memory ..........................80 3.8.2 Portable Pinned Memory .......................81 3.8.3 Mapped Pinned Memory ........................81 3.8.4 Host Memory Registration ......................81 3.9 CUDA Arrays and Texturing .........................82 3.9.1 Texture References ...........................82 3.9.2 Surface References ..........................85 CONTENTS 3.10 Graphics Interoperability........................... 86 3.11 The CUDA Runtime and CUDA Driver API .................87 Chapter 4: Software Environment ................93 4.1 nvcc— CUDA Compiler Driver ........................93 4.2 ptxas-thePTX Assembler ........................ 100 4.3 cuobjdump .................................. 105 4.4 nvidia-smi ................................. 106 4.5 Amazon Web Services ........................... 109 4.5.1 Command-Line Tools ........................ 110 4.5.2 EC2 and Virtualization ........................ 110 4.5.3 Key Pairs ............................... 111 4.5.4 Availability Zones (AZs) and Regions ................ 112 4.5.5 S3 ................................... 112 4.5.6 EBS .................................. 113 4.5.7 AMIs .................................. 113 4.5.8 Linux on EC2 ............................. 114 4.5.9 Windows on EC2 ........................... 115 PART II _____________________________________119 Chapter 5: Memory ........................121 5.1 Host Memory ................................. 122 5.1.1 Allocating Pinned Memory ...................... 122 5.1.2 Portable Pinned Memory ...................... 123 5.1.3 Mapped Pinned Memory ....................... 124 5.1.4 Write-Combined Pinned Memory .................. 124 CONTENTS 5.1.5 Registering Pinned Memory..................... 125 5.1.6 Pinned Memory and UVA ...................... 126 5.1.7 Mapped Pinned Memory Usage ................... 127 5.1.8 NUMA, Thread Affinity, and Pinned Memory ........... 128 5.2 Global Memory ................................ 130 5.2.1 Pointers ................................ 131 5.2.2 Dynamic Allocations ......................... 132 5.2.3 Querying the Amount of Global Memory .............. 137 5.2.4 Static Allocations ........................... 138 5.2.5 MemsetAPIs ............................. 139 5.2.6 Pointer Queries ............................ 140 5.2.7 Peer-to-Peer Access ......................... 143 5.2.8 Reading and Writing Global Memory ................ U3 5.2.9 Coalescing Constraints ....................... 143 5.2.10 Microbenchmarks: Peak Memory Bandwidth .......... 147 5.2.11 Atomic Operations .......................... 152 5.2.12 Texturing from Global Memory .................. 155 5.2.13 ECC (Error Correcting Codes) ................... 155 5.3 Constant Memory .............................. 156 5.3.1 Host and Device __ constant __ Memory ............ 157 5.3.2 Accessing __ constant __ Memory ................ 157 5.4 Local Memory ................................ 158 5.5 Texture Memory ............................... 162 5.6 Shared Memory ............................... 162 5.6.1 Unsized Shared Memory Declarations ............... 163 5.6.2 Warp-Synchronous Coding ..................... 164 5.6.3 Pointers to Shared Memory ..................... 164 CONTENTS 5.7 Memory Copy ................................ 5.7.1 Synchronous versus Asynchronous Memcpy ........... 165 5.7.2 Unified Virtual Addressing...................... 166 5.7.3 CUDA Runtime ............................ 166 5.7.4 DriverAPI............................... 169 Chapter 6: Streams and Events ................. 173 6.1 CPU/GPU Concurrency: Covering Driver Overhead ........... 174 6.1.1 Kernel Launches ........................... 174 6.2 Asynchronous Memcpy ........................... 178 6.2.1 Asynchronous Memcpy: Host-^Device ............... 179 6.2.2 Asynchronous Memcpy: Device-^Host .............. 181 6.2.3 The NULL Stream and Concurrency Breaks ........... 181 6.3 CUDA Events: CPU/GPU Synchronization ................ 183 6.3.1 Blocking Events ........................... 186 6.3.2 Queries ................................ 186 6.4 CUDA Events: Timing ............................ 186 6.5 Concurrent Copying and Kernel Processing ............... 187 6.5.1 concurrencyMemcpyKernel . cu ................ 189 6.5.2 Performance Results ........................ 19Л 6.5.3 Breaking Interengine Concurrency ................. 196 6.6 Mapped Pinned Memory .......................... 197 6.7 Concurrent Kernel Processing ...................... 199 6.8 GPU/GPU Synchronization: cudaStreamWaitEvent ( ) ....... 202 6.8.1 Streams and Events on Multi-GPU: Notes and Limitations . . . 202 6.9 Source Code Reference ........................... 202 CONTENTS Chapter 7: Kernel Execution .................. 205 7.1 Overview ................................... 205 7.2 Syntax ..................................... 206 7.2.1 Limitations ............................... 208 7.2.2 Caches and Coherency ........................ 209 7.2.3 Asynchrony and Error Handling .................. 209 7.2. Д Timeouts ................................ 210 7.2.5 Local Memory ............................. 210 7.2.6 Shared Memory ............................ 211 7.3 Blocks, Threads, Warps, and Lanes .................... 211 7.3.1 Grids of Blocks ............................ 211 7.3.2 Execution Guarantees ........................ 215 7.3.3 Block and Thread IDs ........................ 216 7.Д Occupancy .................................. 220 7.5 Dynamic Parallelism ............................ 222 7.5.1 Scoping and Synchronization .................... 223 7.5.2 Memory Model ............................ 224 7.5.3 Streams and Events ......................... 225 7.5.4 Error Handling ............................ 225 7.5.5 Compiling and Linking ........................ 226 7.5.6 Resource Management ....................... 226 7.5.7 Summary ............................... 228 Chapter 8: Streaming Multiprocessors ............ 231 8.1 Memory .................................... 233 8.1.1 Registers ................................ 233 8.1.2 Local Memory .............................234 CONTENTS 8.1.3 Global Memory............................ 235 8.1.4 Constant Memory........................... 237 8.1.5 Shared Memory............................ 237 8.1.6 Barriers and Coherency ....................... 240 8.2 Integer Support............................... 241 8.2.1 Multiplication ............................. 241 8.2.2 Miscellaneous (Bit Manipulation).................. 242 8.2.3 Funnel Shift (SM 3.5)......................... 243 8.3 Floating-Point Support........................... 244 8.3.1 Formats................................ 244 8.3.2 Single Precision (32-Bit)....................... 250 8.3.3 Double Precision (64-Bit) ...................... 253 8.3.4 Half Precision (16-Bit) ........................ 253 8.3.5 Case Study: f loat-rtial f Conversion .............. 253 8.3.6 Math Library ............................. 258 8.3.7 Additional Reading .......................... 266 8.4 Conditional Code ............................... 267 8.4.1 Predication .............................. 267 8.4.2 Divergence and Convergence .................... 268 8.4.3 Special Cases: Min, Max and Absolute Value ........... 269 8.5 Textures and Surfaces ........................... 269 8.6 Miscellaneous Instructions ........................ 270 8.6.1 Warp-Level Primitives ........................ 270 8.6.2 Block-Level Primitives ....................... 272 8.6.3 Performance Counter ............... 272 8.6.4 Video Instructions ............... 272 CONTENTS 8.6.5 Special Registers........................... 275 8.7 Instruction Sets ............................... 275 Chapter?: Multiple GPUs.................... 287 9.1 Overview ................................... 287 9.2 Peer-to-Peer................................. 288 9.2.1 Peer-to-Peer Memcpy........................ 288 9.2.2 Peer-to-Peer Addressing ...................... 289 9.3 UVA: Inferring Device from Address ................... 291 9.4 Inter-GPU Synchronization ......................... 292 9.5 Single-Threaded Multi-GPU ........................ 294 9.5.1 Current Context Stack ........................ 294 9.5.2 N-Body ................................. 296 9.6 Multithreaded Multi-GPU .......................... 299 Chapter 10: Texturing ...................... 305 10.1 Overview ................................... 305 10.1.1 Two Use Cases ............................ 306 10.2 Texture Memory ............................... 306 10.2.1 Device Memory ........................... 307 10.2.2 CUDA Arrays and Block Linear Addressing ........... 308 10.2.3 Device Memory versus CUDA Arrays ............... 313 10.3 1D Texturing ................................. 3U 10.3.1 Texture Setup ............................ 3U 10.4 Texture as a Read Path ........................... 317 10.4.1 Increasing Effective Address Coverage .............. 318 10.4.2 Texturing from Host Memory ................... 321 CONTENTS 10.5 Texturing with Unnormalized Coordinates ................ 323 10.6 Texturing with Normalized Coordinates ................. 331 10.7 1D Surface Read/Write ........................... 333 10.8 2D Texturing ................................. 335 10.8.1 Microdemo: tex2d_opengl.cu ................. 335 10.9 2D Texturing: Copy Avoidance ....................... 338 10.9.1 2D Texturing from Device Memory ................ 338 10.9.2 2D Surface Read/Write ....................... 340 10.10 3D Texturing ................................. 340 10.11 Layered Textures .............................. 342 10.11.1 1D Layered Textures ........................ 343 10.11.2 2D Layered Textures ........................ 343 10.12 Optimal Block Sizing and Performance .................. 343 10.12.1 Results ................................ 344 10.13 Texturing Quick References ........................ 345 10.13.1 Hardware Capabilities ....................... 345 10.13.2 CUDA Runtime ........................... 347 10.13.3 Driver API .............................. 349 PART III ____________________________________351 Chapter 11: Streaming Workloads ............... 353 11.1 Device Memory ....................... 355 11.2 Asynchronous Memcpy .................. 358 11.3 Streams ...................... 359 11.4 Mapped Pinned Memory ........... 361 11.5 Performance and Summary .............. 362 CONTENTS Chapter 12: Reduction ...................... 365 12.1 Overview ................................... 365 12.2 Two-Pass Reduction ............................ 367 12.3 Single-Pass Reduction ........................... 373 12.4 Reduction with Atomics ........................... 376 12.5 Arbitrary Block Sizes ............................ 377 12.6 Reduction Using Arbitrary Data Types ................... 378 12.7 Predicate Reduction ............................. 382 12.8 Warp Reduction with Shuffle ........................ 382 Chapter 13: Scan ......................... 385 13.1 Definition and Variations .......................... 385 13.2 Overview ................................... 387 13.3 Scan and Circuit Design .......................... 390 13.4 CUDA Implementations ........................... 394 13.4.1 Scan-Then-Fan ........................... 394 13.4.2 Reduce-Then-Scan (Recursive) .................. 400 13.4.3 Reduce-Then-Scan (Two Pass) .................. 403 13.5 Warp Scans ................................. 407 13.5.1 Zero Padding ............................. 408 13.5.2 Templated Formulations ...................... 409 13.5.3 Warp Shuffle ............................. 410 13.5.4 Instruction Counts ......................... 412 13.6 Stream Compaction ............................. 414 13.7 References [Parallel Scan Algorithms) .................. 418 13.8 Further Reading (Parallel Prefix Sum Circuits) ............. 419 CONTENTS Chapter 14: N-Body....................... 421 14.1 Introduction .................................423 ил Л A Matrix of Forces ..........................424 U.2 Naïve Implementation ...........................428 14.3 Shared Memory ...............................432 UÀ Constant Memory ..............................434 14.5 Warp Shuffle .................................436 14.6 Multiple GPUs and Scalability .......................438 14.7 CPU Optimizations ..............................439 14.8 Conclusion ..................................444 14.9 References and Further Reading .....................446 Chapter 15: Image Processing: Normalized Correlation . . 449 15.1 Overview ................................... 449 15.2 Naive Texture-Texture Implementation .................. 452 15.3 Template in Constant Memory ....................... 456 15.4 Image in Shared Memory .......................... 459 15.5 Further Optimizations ............................ 463 15.5.1 SM-Aware Coding .......................... 463 15.5.2. Loop Unrolling ............................ 464 15.6 Source Code ................................. 465 15.7 Performance and Further Reading .................... 466 15.8 Further Reading ............................... 469 Appendix A The CUDA Handbook Library ............471 A.1 Timing .....................................471 A.2 Threading ......................... 472 CONTENTS Α. 3 Driver API Facilities ............................. Д74 A.4 Shmoos .................................... 475 A.5 Command Line Parsing ........................... 476 A.6 Error Handling ................................ 477 Glossary/TLA Decoder ............................. 481 Index ....................................... 487
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spellingShingle	Wilt, Nicholas 1970- The CUDA handbook a comprehensive guide to GPU programming Grafikprozessor (DE-588)4582114-8 gnd Programmierung (DE-588)4076370-5 gnd Parallelverarbeitung (DE-588)4075860-6 gnd CUDA Informatik (DE-588)7719528-0 gnd
subject_GND	(DE-588)4582114-8 (DE-588)4076370-5 (DE-588)4075860-6 (DE-588)7719528-0
title	The CUDA handbook a comprehensive guide to GPU programming
title_auth	The CUDA handbook a comprehensive guide to GPU programming
title_exact_search	The CUDA handbook a comprehensive guide to GPU programming
title_full	The CUDA handbook a comprehensive guide to GPU programming Nicholas Wilt
title_fullStr	The CUDA handbook a comprehensive guide to GPU programming Nicholas Wilt
title_full_unstemmed	The CUDA handbook a comprehensive guide to GPU programming Nicholas Wilt
title_short	The CUDA handbook
title_sort	the cuda handbook a comprehensive guide to gpu programming
title_sub	a comprehensive guide to GPU programming
topic	Grafikprozessor (DE-588)4582114-8 gnd Programmierung (DE-588)4076370-5 gnd Parallelverarbeitung (DE-588)4075860-6 gnd CUDA Informatik (DE-588)7719528-0 gnd
topic_facet	Grafikprozessor Programmierung Parallelverarbeitung CUDA Informatik
url	http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=026076746&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA
work_keys_str_mv	AT wiltnicholas thecudahandbookacomprehensiveguidetogpuprogramming

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