General-purpose GPUs have been widely utilized to accelerate parallel applications. Given a relatively complex programming model and fast architecture evolution, producing efficient GPU code is nontrivial. A variety of simulation and profiling tools have been developed to aid GPU application optimization and architecture design. However, existing tools are either limited by insufficient insights or lacking in support across different GPU architectures, runtime and driver versions. This paper presents CUDAAdvisor, a profiling framework to guide code optimization in modern NVIDIA GPUs. CUDAAdvisor performs various fine-grained analyses based on the profiling results from GPU kernels, such as memory-level analysis (e.g., reuse distance and memory divergence), control flow analysis (e.g., branch divergence) and code-/data-centric debugging. Unlike prior tools, CUDAAdvisor supports GPU profiling across different CUDA versions and architectures, including CUDA 8.0 and Pascal architecture. We demonstrate several case studies that derive significant insights to guide GPU code optimization for performance improvement.