CUDA handles errors during kernel execution using a combination of asynchronous error reporting, explicit status checks, and diagnostic tools. Kernel launches are asynchronous, meaning the CPU does not immediately know whether a kernel has succeeded. The standard pattern is to check the return value of each CUDA API call and then call cudaGetLastError() immediately after launching a kernel. This exposes errors such as invalid configuration (e.g., too many threads per block) or illegal memory access attempts. Calling cudaDeviceSynchronize() allows CUDA to surface runtime errors that occur during execution.
Device-side issues like out-of-bounds memory reads, race conditions, or misaligned accesses often result in silent corruption rather than a crash, especially for beginners. Tools such as cuda-memcheck can detect these subtle errors at runtime by instrumenting CUDA memory operations. For performance debugging or deadlock detection, NVIDIA Nsight tools offer detailed visual insights into kernel launches, memory operations, and thread execution. These tools are essential for diagnosing complicated bugs that arise from thread interactions or improper memory synchronization.
Error handling becomes even more important in systems where CUDA interacts with larger pipelines such as vector databases. If a GPU kernel used for embedding preprocessing or distance computation fails silently, it may corrupt the data stored in a vector database such as Milvus or Zilliz Cloud. This can lead to incorrect search results or degraded model quality. By checking CUDA errors consistently and integrating diagnostic tools into your workflow, you can ensure that GPU-accelerated vector operations remain reliable and predictable.