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CUDA for Engineers gives you direct, hands-on engagement with personal, high-performance parallel computing, enabling you to do computations on a gaming-level PC that would have required a supercomputer just a few years ago.

The authors introduce the essentials of CUDA C programming clearly and concisely, quickly guiding you from running sample programs to building your own code. Throughout, you´ll learn from complete examples you can build, run, and modify, complemented by additional projects that deepen your understanding. All projects are fully developed, with detailed building instructions for all major platforms.

Ideal for any scientist, engineer, or student with at least introductory programming experience, this guide assumes no specialized background in GPU-based or parallel computing. In an appendix, the authors also present a refresher on C programming for those who need it.

Coverage includes

Preparing your computer to run CUDA programs
Understanding CUDA´s parallelism model and C extensions
Transferring data between CPU and GPU
Managing timing, profiling, error handling, and debugging
Creating 2D grids
Interoperating with OpenGL to provide real-time user interactivity
Performing basic simulations with differential equations
Using stencils to manage related computations across threads
Exploiting CUDA´s shared memory capability to enhance performance
Interacting with 3D data: slicing, volume rendering, and ray casting
Using CUDA libraries
Finding more CUDA resources and code
Realistic example applications include

Visualizing functions in 2D and 3D
Solving differential equations while changing initial or boundary conditions
Viewing/processing images or image stacks
Computing inner products and centroids
Solving systems of linear algebraic equations
Monte-Carlo computations



Acknowledgments xvii

About the Authors xix

Introduction 1

What Is CUDA? 1

What Does "Need-to-Knowö Mean for Learning CUDA? 2

What Is Meant by "for Engineersö? 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 6: Reduction and Atomic Functions 121

Threads Interacting Globally 121

Implementing parallel_dot 123

Computing Integral Properties: centroid_2d 130

Summary 138

Suggested Projects 138

References 138

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