Duane Storti, Mete Yurtoglu

CUDA for Engineers (eBook, ePUB)

An Introduction to High-Performance Parallel Computing

• Format: ePub

Produktbeschreibung

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

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Produktdetails

• Verlag: Pearson ITP
• Seitenzahl: 352
• Altersempfehlung: ab 18 Jahre
• Erscheinungstermin: 2. November 2015
• Englisch
• ISBN-13: 9780134177557
• Artikelnr.: 44062568

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 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