This study introduces a comprehensive system de-signed to predict the execution time of k- Wave ultrasound simulations, factoring in the domain size and allocated computing resources. The predictive models, developed using symbolic regression and neural networks, were trained on historical performance data acquired from the Barbora supercomputer. For domain sizes with optimal parameters, the symbolic regression model outperformed, achieving an average error of 5.64 %. Conversely, the neural network showed commendable efficacy in general domain scenarios, with an average error of 8.25 %. Notably, in both instances, the average error remained below the 10% threshold, aligning closely with the uncertainty inherent in the measured data and the execution of real large-scale jobs. Consequently, this predictive system is well-suited for deployment in resource optimization frameworks, significantly enhancing the efficiency of Iarge-scale simulation executions.

Automated execution of computational workflows has become a critical issue in achieving high productivity in various research and development fields. Over the last few years, workflows have emerged as a significant abstraction of numerous real-world processes and phenomena, including digital twins, personalized medicine, and simulation-based science in general. k-Dispatch is a novel tool designed for the efficient offloading of biomedical workflows to remote high-performance computing clusters or cloud. In addition to data transfers, reporting, error handling and remote computations monitoring, k-Dispatch leverages a set of optimizations to dynamically determine suitable execution parameters for individual tasks within workflows, aiming to meet predefined constraints and optimization criteria. k-Dispatch has been successfully deployed within k-Plan, an advanced modelling tool for planning transcranial ultrasound stimulation (TUS) procedures.