  Off-road autonomy validation presents unique challenges due to the
unpredictable and dynamic nature of off-road environments. Traditional methods
focusing on sequentially sweeping across the parameter space for variability
analysis struggle to comprehensively assess the performance and safety of
off-road autonomous systems within the imposed time constraints. This paper
proposes leveraging scalable digital twin simulations within high-performance
computing (HPC) clusters to address this challenge. By harnessing the
computational power of HPC clusters, our approach aims to provide a scalable
and efficient means to validate off-road autonomy algorithms, enabling rapid
iteration and testing of autonomy algorithms under various conditions. We
demonstrate the effectiveness of our framework through performance evaluations
of the HPC cluster in terms of simulation parallelization and present the
systematic variability analysis of a candidate off-road autonomy algorithm to
identify potential vulnerabilities in the autonomy stack's perception, planning
and control modules.
