Efficient use of large-scale supercomputing systems depends on accurate runtime estimation and walltime selection. To avoid premature termination, users often overestimate walltime, leading to poor resource utilization and reduced scheduling efficiency. \new{This paper presents an empirical study of safe walltime recommendation policies that combine runtime prediction with conservative adjustment rules suitable for production HPC environments.}

We evaluate the approach using large-scale production job histories from the Fugaku and NREL Eagle supercomputers, comprising approximately 24M and 11M jobs, respectively.

Runtime prediction models include linear regression, neural networks, and a neural ensemble that improves robustness across workloads. Predicted runtimes are translated into walltime recommendations through bounded adjustment policies.

Experimental results indicate that similarity-aware representations improve prediction accuracy across the evaluated models and time scales, with particularly strong gains for jobs similar to past executions. When combined with the proposed conservative adjustment policies, these improvements translate into significant reductions in wasted walltime ($\approx32\%$) while maintaining a very low risk of job termination across both systems ($<1\%$).