High fidelity fuel performance simulations are a key requirement for the accurate design and safety assessment of nuclear fuel rods, as they allow multidimensional thermo mechanical phenomena to be resolved. Despite their higher predictive capability, such simulations are still rarely adopted in industrial practice due to their high computational cost, which has historically limited fuel performance analyses to simplified and computationally efficient 1.5D approaches. This work addresses this gap by expanding the High Performance Computing (HPC) capabilities of the multidimensional fuel performance code OFFBEAT, enabling its application to large scale and industrially relevant simulations. OFFBEAT has been ported to the modern the ENEA CRESCO8 HPC cluster, where its parallel performance has been systematically assessed. Extensive strong and weak scaling tests have been conducted to evaluate the code scalability and identify performance limitations. The analysis is supported by detailed application profiling, providing quantitative insight into computational hotspots, solver behaviour, and communication overheads. The results highlight linear solvers as the dominant performance bottleneck in high fidelity OFFBEAT simulations. Based on this outcome, a first optimization strategy has been implemented through the integration of PETSc4FOAM library. The impact of this coupling on scalability and computational time is assessed and compared against the standard OFFBEAT solution strategy, showing clear improvements in parallel efficiency and overall performance. By enabling efficient exploitation of HPC resources, this work represents a first concrete step toward extending the range of applicability of OFFBEAT to more complex, higher fidelity simulations, while keeping computational costs compatible with industrial time constraints. These results increase the competitiveness of OFFBEAT as a high fidelity fuel performance tool and lay the groundwork for future large scale and industrial applications.