Challenges in GPU profiling and debugging

Profiling and debugging GPU applications pose challenges compared to CPU programs, as thousands of concurrent threads executing introduce new layers of complexity in understanding, controlling, and diagnosing program behavior. We will highlight a few of these challenges encountered in GPU profiling, such as asynchronous execution of threads, the heterogeneous nature of CUDA code, and the differences between GPU and CPU timing.

Asynchronous execution and concurrency

One of the core challenges in CUDA development arises from the asynchronous execution of GPU operations. When a kernel is launched from the host (CPU), control is almost immediately returned to the host thread, even while the GPU is still executing the kernel. However, this asynchronous execution also makes debugging and reasoning about program behavior difficult. For instance, a kernel that fails on the GPU might not immediately trigger an error on the host, leading to confusing or...