With the rise of new high-performance computing (HPC) accelerators, such as Nvidia and AMD GPUs, the demand for efficient code targeting diverse hardware accelerators poses a critical challenge for HPC application developers. This hardware diversity in the HPC systems necessitates the development of new code tailored to specific architectures, which, in turn, hampers the sustainability of large scientific application development. In this work, we rely on DaCe [1, 2], a data-centric parallel programming framework, to automate the generation of high-performance kernels. DaCe can generate automatic code for multicore processors and various accelerators, alleviating the programmer burden of rewriting code for a new architecture. Our work demonstrates the automatic code generation capabilities of DaCe, applied to a critical high-performance computational kernel for Computational Fluid Dynamics code. Specifically, we focus on the Fortran-based solver, Neko [4] which is based on the Spectral Element Method. This method relies on small-sized matrix multiplications akin to BLAS dgemm operations. We describe the formulation of this computational kernel through DaCe's Stateful Dataflow Multigraph (SDFG) representation. We discuss how this representation facilitates high-performance code generation and detail the workflow for integration of DaCe's automatically generated code into the Neko solver. Initial work on Nvidia GH200. By showcasing the potential of automatic code generation, we highlight the feasibility of supporting the long-term sustainability of large-scale scientific applications by using portable solutions for critical computational kernels of large-scale codes.