Measurement Based Execution Time Analysis of GPGPU Programs via SE+GA

Understanding the execution time is critical for embedded, real-time applications. Worst-case execution time (WCET) is an important metric to check the real-time constraints imposed on embedded applications. For complex execution platforms, such as graphics processing units (GPUs), analysis of WCET imposes great challenges due to the complex characteristics of GPU architecture as well as GPU program semantics. In this paper, we propose GDivAn, a measurement-based WCET analysis tool for arbitrary GPU kernels. GDivAn systematically combines the strength of symbolic execution (SE) and genetic algorithm (GA) to maintain both the scalability and the effectiveness of the analysis process. Our evaluation with several open-source GPU kernels reveals the efficiency of GDivAn.