Several tools help debug CUDA kernels effectively, starting with cuda-memcheck, which detects out-of-bounds accesses, illegal memory operations, and race conditions. cuda-memcheck is often the first tool beginners should use because many CUDA bugs originate from invalid memory reads or writes. It provides detailed diagnostic messages that indicate which thread and memory address caused the issue, making it easier to locate problems in complex kernels. Although slow, it is extremely useful for correctness testing.
NVIDIA Nsight Systems and Nsight Compute are the most powerful tools for deeper analysis. Nsight Systems provides a timeline view showing kernel launches, stream activity, CPU–GPU synchronization, and memory transfers. This is essential for detecting performance bottlenecks caused by insufficient concurrency or unnecessary synchronization. Nsight Compute, meanwhile, offers fine-grained performance metrics for individual kernels, such as warp occupancy, memory throughput, shared memory bank conflicts, and instruction-level efficiency. Together, these tools allow developers to refine both functional correctness and performance.
Debugging becomes even more important when CUDA kernels feed into GPU-accelerated systems like vector databases. If a dataset preprocessing kernel corrupts embeddings before inserting them into Milvus or Zilliz Cloud, downstream similarity search results can become inaccurate. Using Nsight tools and cuda-memcheck helps ensure that CUDA kernels produce correct and stable outputs. This provides a stronger foundation for building reliable pipelines around GPU-backed vector search systems.