Air pollution – indoor and outdoor – is a major contributor to the worldwide burden of disease and a pervasive risk factor to lower respiratory infections, pulmonary diseases, lung cancer and multiple cross-infections [1]. In a society that spends 90% of the time indoors, indoor air quality (IAQ) control performed through building ventilation becomes the focus of attention. Computational fluid dynamics (CFD) is usually used to design natural and mechanically advanced ventilation strategies through Reynolds-Averaged Navier-Stokes (RANS) or Large Eddy Simulation (LES). CFD has proven accurate in predicting detailed characteristics of indoor flow field, heterogeneous distributions of air temperature, pollutants – viral or otherwise – and their source-to-dose paths. To ease the computational burden presented by numerical modelling, especially LES, High Performance Computing (HPC) is essential to model complex models in terms of data analysis, processing and speed. Furthermore, the dynamic relationship between IAQ and varying indoor conditions to obtain optimum environment design with minimal cross-infection should also be considered. This research adopted the modelling framework CUBE [2] combined with Fugaku/A64FX’s HPC application-centric design to quantify inhalation risk through interacting virtual manikins in different ventilation strategies. Results showed that stratified distributions such as displacement and stratum reduce infection risk considerably. Additionally, this research further aimed to find optimum indoor airflow rate and inlet/outlet locations focusing on displacement ventilation through a multi-objective optimization method to find the optimal balance between thermal-comfort and infection risk.