Ideal for students with at least introductory programming experience, this tutorial presents examples and reusable C code to jumpstart a wide variety of applications. Students will walk through moving from serial to parallel computation; computing values of a function in parallel; understanding 2D parallelism; simulating dynamics in the phase plane; simulating heat conduction; interacting with 3D data; walking through a basic N-body simulation, and more.
Ideal for students with at least introductory programming experience, this tutorial presents examples and reusable C code to jumpstart a wide variety of applications. Students will walk through moving from serial to parallel computation; computing values of a function in parallel; understanding 2D parallelism; simulating dynamics in the phase plane; simulating heat conduction; interacting with 3D data; walking through a basic N-body simulation, and more. Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
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Autorenporträt
Duane Storti is a professor of mechanical engineering at the University of Washington in Seattle. He has thirty-five years of experience in teaching and research in the areas of engineering mathematics, dynamics and vibrations, computer-aided design, 3D printing, and applied GPU computing. Mete Yurtoglu is currently pursuing an M.S. in applied mathematics and a Ph.D. in mechanical engineering at the University of Washington in Seattle. His research interests include GPU-based methods for computer vision and machine learning.
Inhaltsangabe
Acknowledgments xvii About the Authors xix
Introduction 1 What Is CUDA? 1 What Does “Need-to-Know” Mean for Learning CUDA? 2 What Is Meant by “for Engineers”? 3 What Do You Need to Get Started with CUDA? 4 How Is This Book Structured? 4 Conventions Used in This Book 8 Code Used in This Book 8 User’s Guide 9 Historical Context 10 References 12
Chapter 1: First Steps 13 Running CUDA Samples 13 Running Our Own Serial Apps 19 Summary 22 Suggested Projects 23
Chapter 2: CUDA Essentials 25 CUDA’s Model for Parallelism 25 Need-to-Know CUDA API and C Language Extensions 28 Summary 31 Suggested Projects 31 References 31
Chapter 3: From Loops to Grids 33 Parallelizing dist_v1 33 Parallelizing dist_v2 38 Standard Workflow 42 Simplified Workflow 43 Summary 47 Suggested Projects 48 References 48
Chapter 4: 2D Grids and Interactive Graphics 49 Launching 2D Computational Grids 50 Live Display via Graphics Interop 56 Application: Stability 66 Summary 76 Suggested Projects 76 References 77
Chapter 5: Stencils and Shared Memory 79 Thread Interdependence 80 Computing Derivatives on a 1D Grid 81 Summary 117 Suggested Projects 118 References 119
Chapter 7: Interacting with 3D Data 141 Launching 3D Computational Grids: dist_3d 144 Viewing and Interacting with 3D Data: vis_3d 146 Summary 171 Suggested Projects 171 References 171
Chapter 8: Using CUDA Libraries 173 Custom versus Off-the-Shelf 173 Thrust 175 cuRAND 190 NPP 193 Linear Algebra Using cuSOLVER and cuBLAS . 201 cuDNN 207 ArrayFire 207 Summary 207 Suggested 208 References 209
Chapter 9: Exploring the CUDA Ecosystem 211 The Go-To List of Primary Sources 211 Further Sources 217 Summary 218 Suggested Projects 219
Appendix A: Hardware Setup 221 Checking for an NVIDIA GPU: Windows 221 Checking for an NVIDIA GPU: OS X 222 Checking for an NVIDIA GPU: Linux 223 Determining Compute Capability 223 Upgrading Compute Capability 225
Appendix B: Software Setup 229 Windows Setup 229 OS X Setup 238 Linux Setup 240
Appendix C: Need-to-Know C Programming 245 Characterization of C 245 C Language Basics 246 Data Types, Declarations, and Assignments 248 Defining Functions 250 Building Apps: Create, Compile, Run, Debug 251 Arrays, Memory Allocation, and Pointers 262 Control Statements: for, if 263 Sample C Programs 267 References 277
Appendix D: CUDA Practicalities: Timing, Profiling, Error Handling, and Debugging 279 Execution Timing and Profiling 279 Error Handling 292 Debugging in Windows 298 Debugging in Linux 305 CUDA-MEMCHECK 308 Using Visual Studio Property Pages 309 References 312
Introduction 1 What Is CUDA? 1 What Does “Need-to-Know” Mean for Learning CUDA? 2 What Is Meant by “for Engineers”? 3 What Do You Need to Get Started with CUDA? 4 How Is This Book Structured? 4 Conventions Used in This Book 8 Code Used in This Book 8 User’s Guide 9 Historical Context 10 References 12
Chapter 1: First Steps 13 Running CUDA Samples 13 Running Our Own Serial Apps 19 Summary 22 Suggested Projects 23
Chapter 2: CUDA Essentials 25 CUDA’s Model for Parallelism 25 Need-to-Know CUDA API and C Language Extensions 28 Summary 31 Suggested Projects 31 References 31
Chapter 3: From Loops to Grids 33 Parallelizing dist_v1 33 Parallelizing dist_v2 38 Standard Workflow 42 Simplified Workflow 43 Summary 47 Suggested Projects 48 References 48
Chapter 4: 2D Grids and Interactive Graphics 49 Launching 2D Computational Grids 50 Live Display via Graphics Interop 56 Application: Stability 66 Summary 76 Suggested Projects 76 References 77
Chapter 5: Stencils and Shared Memory 79 Thread Interdependence 80 Computing Derivatives on a 1D Grid 81 Summary 117 Suggested Projects 118 References 119
Chapter 7: Interacting with 3D Data 141 Launching 3D Computational Grids: dist_3d 144 Viewing and Interacting with 3D Data: vis_3d 146 Summary 171 Suggested Projects 171 References 171
Chapter 8: Using CUDA Libraries 173 Custom versus Off-the-Shelf 173 Thrust 175 cuRAND 190 NPP 193 Linear Algebra Using cuSOLVER and cuBLAS . 201 cuDNN 207 ArrayFire 207 Summary 207 Suggested 208 References 209
Chapter 9: Exploring the CUDA Ecosystem 211 The Go-To List of Primary Sources 211 Further Sources 217 Summary 218 Suggested Projects 219
Appendix A: Hardware Setup 221 Checking for an NVIDIA GPU: Windows 221 Checking for an NVIDIA GPU: OS X 222 Checking for an NVIDIA GPU: Linux 223 Determining Compute Capability 223 Upgrading Compute Capability 225
Appendix B: Software Setup 229 Windows Setup 229 OS X Setup 238 Linux Setup 240
Appendix C: Need-to-Know C Programming 245 Characterization of C 245 C Language Basics 246 Data Types, Declarations, and Assignments 248 Defining Functions 250 Building Apps: Create, Compile, Run, Debug 251 Arrays, Memory Allocation, and Pointers 262 Control Statements: for, if 263 Sample C Programs 267 References 277
Appendix D: CUDA Practicalities: Timing, Profiling, Error Handling, and Debugging 279 Execution Timing and Profiling 279 Error Handling 292 Debugging in Windows 298 Debugging in Linux 305 CUDA-MEMCHECK 308 Using Visual Studio Property Pages 309 References 312
Index 313
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