Debugging CUDA applications can be one of the most frustrating and time-consuming challenges in GPU development. Race conditions, memory access violations, and performance bottlenecks can silently cripple your code, leading to unexpected crashes, incorrect results, and inefficiencies. Without the right debugging strategies, even the most experienced developers struggle to pinpoint and resolve these issues.

CUDA C++ Debugging: A Safer GPU Kernel Programming is your practical guide to identifying, diagnosing, and resolving errors in CUDA applications. Whether you're a beginner struggling with memory leaks or an advanced developer optimizing multi-GPU workloads, this book will equip you with battle-tested debugging techniques used by professionals in AI, scientific computing, and high-performance graphics.

✔ Detect and fix memory access violations to prevent segmentation faults and undefined behavior.
✔ Identify and resolve race conditions and deadlocks that lead to unpredictable program execution.
✔ Optimize performance by analyzing warp divergence, occupancy, and memory bottlenecks.
✔ Use CUDA error handling best practices to catch issues before they cause major failures.
✔ Leverage powerful debugging tools like Nsight, cuda-gdb, and Compute Sanitizer for real-time analysis.
✔ Debug multi-GPU and stream-based applications to scale efficiently across large workloads.
✔ Apply preventative debugging strategies to write safer, more reliable CUDA code from the start.

Unlike generic CUDA programming books, this guide focuses entirely on debugging and error handling, providing real-world case studies, hands-on examples, and expert insights to help you overcome common pitfalls. Every chapter is designed to improve your confidence in writing, testing, and deploying CUDA applications with minimal debugging headaches.

If you're a GPU programmer, data scientist, or AI engineer looking to write safer, faster, and more reliable CUDA code, this book is your go-to resource for mastering CUDA debugging.