NVIDIA CUDA Programming Guide

The NVIDIA CUDA Programming Guide is the official reference for developing high-performance applications using NVIDIA’s parallel computing platform and GPU architecture. It explains the CUDA programming model, memory hierarchy, kernel execution, and optimization techniques for accelerating computation. Designed for software engineers and researchers, the guide provides detailed instructions, code examples, and best practices for leveraging CUDA to achieve maximum performance on NVIDIA GPUs.

NVIDIA Corporation — CUDA Platform Engineering and Developer Technology Team

1. Introduction
• Overview of CUDA and GPU Computing
• Key Concepts: Host vs. Device
• Parallel Programming Model Overview

2. Programming Model
• CUDA Thread Hierarchy (Grids, Blocks, Threads)
• Kernels and Execution Configuration
• Memory Spaces and Data Movement
• Synchronization and Communication

3. Hardware Model
• GPU Architecture Overview
• Streaming Multiprocessors (SMs)
• Warps, Registers, and Occupancy
• Memory Hierarchy and Bandwidth Considerations

4. CUDA Runtime API
• Managing Devices and Contexts
• Memory Allocation and Transfers
• Launching Kernels
• Error Handling and Synchronization

5. Memory Management
• Global, Shared, Constant, and Texture Memory
• Memory Coalescing and Access Patterns
• Unified Memory Model
• Caching and Optimization Techniques

6. Performance and Optimization
• Profiling and Bottleneck Identification
• Warp Divergence and Thread Scheduling
• Instruction-Level Optimization
• Using CUDA Streams and Concurrency

7. Advanced Features
• Dynamic Parallelism
• Cooperative Groups
• CUDA Graphs
• Multi-GPU Programming

8. Libraries and Tools
• cuBLAS, cuFFT, cuDNN, and Other Core Libraries
• CUDA Toolkit Utilities
• Nsight Compute and Nsight Systems

9. Best Practices
• Coding Guidelines
• Debugging and Testing
• Portability and Compatibility

10. Appendices
• API References and Data Types
• Deprecated Features
• Version History
• Glossary of Terms