The HighPerMeshes framework for numerical algorithms on unstructured grids. (21st September 2021)

• Record Type: Journal Article
• Authors: Alhaddad, Samer; Förstner, Jens; Groth, Stefan; Grünewald, Daniel; Grynko, Yevgen; Hannig, Frank; Kenter, Tobias; Pfreundt, Franz‐Josef; Plessl, Christian; Schotte, Merlind; Steinke, Thomas; Teich, Jürgen; Weiser, Martin; Wende, Florian
• Other Names: Wu Chase guestEditor.; Yildirim Tulay guestEditor.; Ivanovic Mirjana guestEditor.; Bellatreche Ladjel guestEditor.; Wyrzykowski Roman guestEditor.; Ciorba Florina M. guestEditor.
• Is Part Of: Concurrency and computation. Volume 34:Number 14(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-09-21
• Subjects: code generation -- distributed computing -- domain‐specific languages -- numerical algorithms; Parallel processing (Electronic computers) -- Periodicals; Parallel computers -- Periodicals; 004.35
• DOI: 10.1002/cpe.6616
• Languages: English
• ISSNs: 1532-0626

Abstract

Summary: Solving partial differential equations (PDEs) on unstructured grids is a cornerstone of engineering and scientific computing. Heterogeneous parallel platforms, including CPUs, GPUs, and FPGAs, enable energy‐efficient and computationally demanding simulations. In this article, we introduce the HighPerMeshes C++ ‐embedded domain‐specific language (DSL) that bridges the abstraction gap between the mathematical formulation of mesh‐based algorithms for PDE problems on the one hand and an increasing number of heterogeneous platforms with their different programming models on the other hand. Thus, the HighPerMeshes DSL aims at higher productivity in the code development process for multiple target platforms. We introduce the concepts as well as the basic structure of the HighPerMeshes DSL, and demonstrate its usage with three examples. The mapping of the abstract algorithmic description onto parallel hardware, including distributed memory compute clusters, is presented. A code generator and a matching back end allow the acceleration of HighPerMeshes code with GPUs. Finally, the achievable performance and scalability are demonstrated for different example problems.