Exam NCP-AII Topic 1 Question 102 Discussion

Actual exam question for NVIDIA's NCP-AII exam
Question #: 102
Topic #: 1

You're optimizing an Intel Xeon server with 4 NVIDIAAIOO GPUs for a computer vision application that uses CODA. You notice that the GPU utilization is fluctuating significantly, and performance is inconsistent. Using 'nvprof, you identify that there are frequent stalls in the CUDA kernels due to thread divergence. What are possible causes and solutions?

A. The input data is not properly aligned in memory. Ensure that data is aligned to 128-byte boundaries using aligned memory allocation techniques. B. The CUDA code contains conditional branches that lead to different execution paths for different threads within the same warp. Rewrite the CUDA code to minimize branching and favor uniform execution paths within warps. C. The GPUs are overheating, causing thermal throttling. Improve the server's cooling. D. The CUDA compiler is generating suboptimal code. Try using different compiler optimization flags (e.g., '-O3' or '-ftz=true'). E. The CUDA driver version is incompatible with the CUDA toolkit version. Update the CUDA driver to a compatible version.

Suggested Answer: A,B Vote an answer

Thread divergence occurs when threads within the same warp take different execution paths due to conditional branches, leading to serialization. Re-writing code to minimize branching is critical. Memory alignment ensures that threads access memory efficiently and doesn't cause thread divergence stalls, which is often misaligned. Thermal throttling is a possible cause of fluctuating utilization but doesn't directly explain the stalls identified in the profiler. Compiler flags and driver version issues can cause performance problems but are less likely to cause frequent thread divergence stalls.

by Dwight at Feb 21, 2026, 09:22 PM

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