Why profiling and debugging matter
GPUs are massive parallel processors capable of executing tens of thousands of lightweight threads simultaneously. However, achieving peak performance is challenging and requires a deep understanding of how the GPU schedules threads, manages memory, and communicates with the CPU. Even small suboptimal memory access patterns or excessive synchronization can result in noticeable slowdowns that waste computational resources and energy.
This is where profiling becomes essential. Profiling reveals what our code is actually doing on the GPU rather than what we think it is doing. Particularly, it provides quantitative insights into performance behavior such as which kernels dominate execution time, how efficiently GPU resources are utilized, where memory bottlenecks occur, and how data transfers overlap with computation. Optimization efforts without profiling are often reduced to guesswork, as we may spend hours rewriting code that is already efficient, while...
