NVIDIA CUDA remains the foundation of GPU-accelerated computing, powering everything from scientific simulations to large-scale AI training.

But writing correct, maintainable, and performant CUDA code can be challenging: memory bugs hide in plain sight, performance bottlenecks can be invisible without the right instrumentation, and hand-rolled GPU algorithms rarely match the efficiency of optimized libraries. Fortunately, the modern CUDA toolchain has matured significantly, and many of these challenges now have straightforward solutions.

In this blog post, we will walk through the tools NVIDIA offers to debug, benchmark, and improve your code.…