GridapDistributed: a massively parallel finite element toolbox in Julia

The ever-increasing demand for resolution and accuracy in mathematical models of physical processes governed by systems of Partial Differential Equations (PDEs) can only be addressed using fully-parallel advanced numerical discretization methods and scalable solution methods, thus able to exploit the vast amount of computational resources in state-of-the-art supercomputers. To this end, GridapDistributed is a registered Julia (Bezanson et al., 2017) software package which provides fully-parallel distributed memory data structures and associated methods for the Finite Element (FE) numerical solution of PDEs on parallel computers. Thus, it can be run on multi-core CPU desktop computers at small scales, as well as on HPC clusters and supercomputers at medium/large scales. The data structures in GridapDistributed are designed to mirror as far as possible their counterparts in the Gridap (Badia & Verdugo, 2020) Julia software package, while implementing/leveraging most of their abstract interfaces (see Francesc Verdugo & Badia (2022) for a detailed overview of the software design of Gridap). As a result, sequential Julia scripts written in the high-level Application Programming Interface (API) of Gridap can be used verbatim up to minor adjustments in a parallel distributed memory context using GridapDistributed. This equips end-users with a tool for the development of simulation codes able to solve real-world application problems on massively parallel supercomputers while using a highly expressive, compact syntax that resembles mathematical notation. This is indeed one of the main advantages of GridapDistributed and a major design goal that we pursue.