Beyond Exact: Tight WCET Analysis of GPU Kernels with Branch Divergence

The increasing adoption of GPUs in real-time systems necessitates precise timing analysis for GPU thread blocks to ensure overall system predictability. However, the uncertainties of branch executions within GPU warps impose significant barriers for predicting the worst-case execution time (WCET) of the thread block. Existing WCET analysis for thread blocks typically assumes deterministic warp execution paths, which is unrealistic given the dynamic control flows of threads within the warps. Moreover, as the warp scheduler operates as a black box, the analysis must rely on relaxed scheduling assumptions, resulting in overly-pessimistic bounds in order to cover edge scenarios. This paper first establishes the need for static analysis by showing how branch divergence can trigger timing anomalies and influence block WCETs. We then develop an exact WCET analysis for a GPU thread block under the same scheduling constraints as prior work while not imposing any warp execution path assumptions. Furthermore, by enforcing a practical constraint on warp executions, we present a tighter analysis that enhances system predictability. Experiments show that the proposed analysis under warp execution constraints significantly reduces the WCET estimations of GPU thread blocks (by 19.13% on average and up to 39.39%).