This guide provides practical advice for setting up a GPU kernel development environment, focusing on common pitfalls. It details how to check GPU compute capabilities and compatibility with CUDA versions, emphasizing that driver, toolkit, and runtime versions are distinct. The guide also explains how to accurately benchmark asynchronous GPU operations using CUDA events and addresses permission errors when accessing profiler counters. Finally, it suggests a smoke test to ensure the entire development stack is functional. AI
IMPACT Provides essential setup guidance for developers working with GPU kernels, crucial for optimizing AI model training and inference.
RANK_REASON The item is a practical guide for setting up a development environment, not a release of new technology.
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