GPU Programming Using CUDA C/C++ for Parallel Computing

About this Course

This practical overview presents GPU Programming Using CUDA C/C++ by Ahmad Abdelfattah, a focused guide that connects GPU architecture to production-ready CUDA code. It emphasizes hands-on learning: concise explanations of streaming multiprocessors, warps, and memory hierarchies are paired with short code examples and small projects that show how architectural decisions shape performance and correctness. The narrative guides you from parallel thinking to deployable kernels, with a strong measurement-first mindset.

Learning Outcomes

After studying this material you will be able to translate CPU algorithms into CUDA kernels, manage host–device interactions safely, and use profiling evidence to prioritize optimizations. Expect to:

• Design parallel work decomposition using threads, blocks, and grids to express data and task parallelism.
• Write and launch CUDA kernels reliably, handle API errors, and synchronize across device and host.
• Implement memory optimizations: coalesced global accesses, shared-memory tiling, and selective use of pinned or unified memory to reduce transfer overhead.
• Profile with tools like Nsight to identify hotspots, then apply tuning strategies such as occupancy balancing, register usage control, and loop tiling.
• Adopt debugging and validation workflows to detect race conditions, memory mistakes, and correctness regressions.

Who This Helps

The material is geared to software developers and engineers who already know C/C++ and want to add GPU acceleration to their projects. It suits:

• Developers new to CUDA who need a structured, example-driven introduction to parallel programming concepts.
• Intermediate engineers porting CPU hotspots to GPUs and seeking measurement-driven optimization approaches.
• Experienced practitioners looking for a compact, practical reference on memory-access patterns, asynchronous execution, and common performance trade-offs.

Teaching Approach

The guide favors short, focused units: a core architectural concept, a minimal code snippet that demonstrates the idea, and a brief exercise to apply it. This incremental method reduces cognitive load and enables quick feedback loops—compile, run, profile, and refine—so you can iterate toward effective, maintainable GPU kernels.

Core Techniques Covered

Coverage centers on techniques you can apply immediately to real workloads: optimizing memory layout for coalesced accesses, using shared memory to improve locality, choosing grid and block dimensions to balance occupancy and register pressure, and overlapping compute with data transfers via streams. Profiling and debugging workflows are integrated so performance tuning is data-driven rather than speculative.

Common Pitfalls and Defensive Patterns

The material highlights frequent mistakes—non-coalesced accesses, overlooked error checks, improper synchronization, and inefficient allocation patterns—and prescribes defensive idioms. Code snippets demonstrate robust API error handling, safe synchronization, and validation steps that reduce debugging time in early development.

Hands-on Exercises

Short, goal-oriented tasks reinforce concepts: implement a parallel vector addition, convert a CPU image filter into a tiled CUDA kernel, and prototype a compact GPU-backed training loop to explore transfer vs. compute trade-offs. Each exercise encourages profiling first so you learn to quantify and verify speedups.

Practical Next Steps

To get the most from the guide, run examples on a CUDA-capable system and follow a profiling-driven development loop: identify a hotspot, implement a kernel, measure with profiling tools, and refine memory access and launch parameters. This workflow cultivates both competence and confidence for production projects.

Quick Tips

• Profile before optimizing—target real bottlenecks with Nsight or similar tools.
• Prioritize data layout and reuse before micro-tuning launch parameters.
• Keep kernels small and testable; include API error checks to simplify debugging.

Final Note

Concise and example-rich, this overview guides you from core CUDA concepts to applied optimization. If your goal is to write efficient CUDA C/C++ kernels and integrate GPU acceleration into C/C++ applications, the material offers a practical, actionable path to achieve measurable performance improvements.