At this level, individuals are expected to have a basic understanding of the concept of parallel computing and the CUDA platform. They should be familiar with the idea of GPU programming but may not yet have practical experience in writing or running CUDA programs.

Novices should be able to install the CUDA toolkit and SDK, understand the CUDA memory hierarchy, and know the basic CUDA syntax. They should be capable of writing, compiling, and running simple CUDA programs, and have an understanding of thread hierarchy and block dimensions.

Intermediate users should be proficient in using CUDA libraries and optimizing memory usage. They should understand CUDA streams and concurrency, have experience debugging CUDA code, and be able to use shared and constant memory spaces effectively.

Advanced users should be proficient in optimizing CUDA code for performance and understand advanced CUDA features like dynamic parallelism. They should be able to use CUDA in combination with other programming languages, have experience with multi-GPU programming, and understand CUDA device query and capabilities.

Experts should have a deep understanding of GPU architecture and how it affects CUDA programming. They should be able to design and implement complex algorithms in CUDA, use CUDA for machine learning and AI applications, and have experience with advanced CUDA debugging and profiling tools. Experts should also be able to teach and mentor others in CUDA programming.