CUDA Programming with C++: From Basics to Expert Proficiency. ADVANCED TOPICS IN SCIENCE AND TECHNOLOGY

Contents....3

Introduction....7

1 Introduction to CUDA Programming and C++....11

1.1 The Evolution of Parallel Computing....11

1.2 Overview of CUDA and GPU Computing....12

1.3 Why Use CUDA with C++?....14

1.4 Basic Concepts and Terminology....16

1.5 Installing and Setting Up CUDA Toolkit....18

1.6 First CUDA Program: Hello World....19

1.7 Understanding CUDA Development Workflow....21

1.8 Brief Introduction to C++ Concepts for CUDA....22

1.9 Compiling and Running CUDA Programs....25

1.10 Common Tools and Resources for CUDA Development....27

2 CUDA Architecture and GPU Computing....30

2.1 Understanding GPU Architecture....30

2.2 CUDA Programming Model vs CPU Programming....31

2.3 CUDA Cores and Thread Hierarchy....33

2.4 Warp and Block Scheduling....35

2.5 Memory Architecture in GPUs....36

2.6 CUDA Execution Model....38

2.7 Global, Shared, and Constant Memory....40

2.8 Streaming Multiprocessors (SMs)....41

2.9 Understanding the CUDA Compute Capability....43

2.10 Introduction to NVIDIA’s GPU Hardware Models....45

3 Setting Up Your Development Environment....49

3.1 System Requirements for CUDA Development....49

3.2 Installing CUDA Toolkit on Windows....50

3.3 Installing CUDA Toolkit on Linux....52

3.4 Installing CUDA Toolkit on macOS....54

3.5 Installing NVIDIA Drivers....55

3.6 Setting Up Integrated Development Environments (IDEs)....56

3.7 Configuring Environment Variables....59

3.8 Testing the Installation....60

3.9 Updating and Uninstalling CUDA Toolkit....62

3.10 Using Docker for CUDA Development....64

4 Understanding CUDA Kernels and Threads....68

4.1 What is a CUDA Kernel?....68

4.2 Writing Your First CUDA Kernel....69

4.3 Launching CUDA Kernels....70

4.4 Understanding CUDA Threads....72

4.5 Thread Indexing and Mapping....73

4.6 Block and Grid Dimensions....75

4.7 Synchronizing Threads....76

4.8 Shared Memory and Thread Cooperation....78

4.9 Thread Divergence....80

4.10 Best Practices for Designing CUDA Kernels....82

5 Memory Management in CUDA....86

5.1 Overview of Memory Types in CUDA....86

5.2 Global Memory Management....87

5.3 Shared Memory Management....89

5.4 Constant Memory Management....90

5.5 Texture Memory and Surface Memory....92

5.6 Unified Memory in CUDA....93

5.7 Memory Allocation and Deallocation....95

5.8 Memory Transfers Between Host and Device....97

5.9 Optimizing Memory Access Patterns....99

5.10 Avoiding and Handling Memory Errors....101

6 CUDA Parallel Programming Models....105

6.1 Introduction to Parallel Programming Models....105

6.2 Single Instruction Multiple Threads (SIMT)....106

6.3 Thread and Data Parallelism....108

6.4 Domain Decomposition....110

6.5 Task Parallelism....111

6.6 Hybrid Parallelism Models....113

6.7 Using Streams for Parallel Execution....115

6.8 Hierarchical Grid Execution....117

6.9 Multi-GPU Parallel Programming....118

6.10 Best Practices for Choosing a Parallel Programming Model....120

7 Optimizing CUDA Performance....123

7.1 Introduction to CUDA Performance Optimization....123

7.2 Profiling CUDA Applications....124

7.3 Optimizing Memory Access Patterns....126

7.4 Reducing Memory Transfers....128

7.5 Improving Instruction Throughput....130

Compiler Optimizations....130

Loop Unrolling....130

Instruction Scheduling....131

Reducing Divergence....131

Utilizing CUDA Streams....132

7.6 Optimizing Kernel Launch Configuration....132

7.7 Latency Hiding Techniques....134

7.8 Caching and Shared Memory Techniques....136

7.9 Occupancy and Resource Utilization....138

7.10 Load Balancing and Reducing Divergence....141

8 Advanced CUDA Programming Techniques....145

8.1 Introduction to Advanced CUDA Programming....145

8.2 Dynamic Parallelism....146

8.3 CUDA Graphs....148

8.4 Unified Memory and Memory Oversubscription....149

8.5 CUDA Streams and Asynchronous Execution....151

8.6 Peer-to-Peer Memory Access....153

8.7 Inter-Process Communication....155

8.8 CUDA and Multi-GPU Programming....157

8.9 Using Thrust Library for High-Level Abstractions....158

8.10 CUDA in Heterogeneous Computing Environments....160

9 Debugging and Profiling CUDA Applications....164

9.1 Introduction to Debugging and Profiling....164

9.2 Common CUDA Errors and How to Fix Them....165

9.3 Using NVIDIA Nsight for Debugging....167

9.4 Manual Debugging Techniques....169

9.5 Using CUDA-GDB Debugger....171

9.6 Analyzing Kernel Performance with NVIDIA Visual Profiler....173

9.7 Profiling CPU-GPU Interactions....175

9.8 Interpreting Profiling Results....177

9.9 Optimizing Code Based on Profiling Data....178

9.10 Best Practices for Efficient Debugging and Profiling....180

10 Case Studies and Real-World Applications....184

10.1 Introduction to Real-World CUDA Applications....184

10.2 Case Study: Image Processing and Computer Vision....185

10.3 Case Study: Scientific Computing and Simulations....187

10.4 Case Study: Deep Learning and Neural Networks....188

10.5 Case Study: Real-Time Rendering and Graphics....190

10.6 Case Study: Financial Modeling and Risk Analysis....192

10.7 Case Study: Bioinformatics and Genomics....194

10.8 Case Study: Autonomous Vehicles and Robotics....197

10.9 Case Study: Big Data Analytics....199

10.10 Future Trends and Developments in CUDA....201

"CUDA Programming with C++: From Basics to Expert Proficiency" is a comprehensive guide aimed at providing a deep understanding of parallel computing using CUDA and C++. Tailored for both beginners and experienced developers, this book meticulously covers fundamental concepts, advanced techniques, and practical applications of CUDA programming. From setting up the development environment to understanding GPU architecture, managing memory, and optimizing performance, each chapter is designed to build a robust foundation and advance progressively in complexity.

The book also delves into real-world applications and case studies across various industries, showcasing the transformative potential of CUDA in fields like scientific computing, deep learning, and real-time rendering. Whether you are a student, researcher, or professional developer, "CUDA Programming with C++" equips you with the knowledge and skills to harness the full power of GPU computing, enabling you to design, optimize, and deploy high-performance applications efficiently.