A fast and flexible CFD solver with heterogeneous execution
Gabriel Weymouth, Bernat Font
The growth of computational power driven by novel accelerator architectures has pushed physics solvers to transition from their traditional multi-CPU approach to GPU-ready codebases. Moreover, the integration of data-driven models, and in particular machine learning (ML), into physics solvers limits the choice of programming languages that can natively offer both speed and such high-level libraries.
Julia for High-Performance Computing
Function (4.1)
