Asynchronous distributed-memory task-parallel algorithm for compressible flows on unstructured 3D Eulerian grids. (October 2021)

• Record Type: Journal Article
• Authors: Bakosi, J.; Bird, R.; Gonzalez, F.; Junghans, C.; Li, W.; Luo, H.; Pandare, A.; Waltz, J.
• Is Part Of: Advances in engineering software. Volume 160(2021)
• Publication Date: 2021-10
• Subjects: Shock hydrodynamics -- Finite element method -- Flux-corrected transport -- Charm++ -- Automatic load balancing; Computer-aided engineering -- Periodicals; Engineering -- Computer programs -- Periodicals; Engineering -- Software -- Periodicals; Periodicals; 620.0028553
• DOI: 10.1016/j.advengsoft.2020.102962
• Languages: English
• ISSNs: 0965-9978

Abstract

Highlights: .A finite element method for the simulation of compressible flows using the Charm++ runtime system has been implemented. Strong and weak scalability up to and computational cells, respectively, have been demonstrated. The benefits of automatic load balancing in Charm++ have been demonstrated. The full source code is available at quinoacomputing.org. Abstract: We discuss the implementation of a finite element method, used to numerically solve the Euler equations of compressible flows, using an asynchronous runtime system (RTS). The algorithm is implemented for distributed-memory machines, using stationary unstructured 3D meshes, combining data-, and task-parallelism on top of the Charm++ RTS. Charm++'s execution model is asynchronous by default, allowing arbitrary overlap of computation and communication. Task-parallelism allows scheduling parts of an algorithm independently of, or dependent on, each other. Built-in automatic load balancing enables continuous redistribution of computational load by migration of work units based on real-time CPU load measurement. The RTS also features automatic checkpointing, fault tolerance, resilience against hardware failure, and supports power-, and energy-aware computation. We demonstrate scalability up to 25 × 10 9 cells at O ( 10 4 ) compute cores and the benefits of automatic load balancing for irregular workloads. The full source code with documentation is available at https://quinoacomputing.org .