A parallel implementation strategy for meshless methods based on the functional programming paradigm. (January 2021)

• Record Type: Journal Article
• Authors: Barbosa, Marlucio; Telles, Jose Claudio de Faria JCF Telles; Santiago, Jose Antonio Fontes; Fontes Junior, Edivaldo Figueiredo; Costa, Edmundo Guimarães de Araújo
• Is Part Of: Advances in engineering software. Volume 151(2021)
• Publication Date: 2021-01
• Subjects: Meshless method -- Functional programming -- Massively parallel programming -- Meshless local Ppetrov-Galerkin (MLPG) method; Computer-aided engineering -- Periodicals; Engineering -- Computer programs -- Periodicals; Engineering -- Software -- Periodicals; Periodicals; 620.0028553
• DOI: 10.1016/j.advengsoft.2020.102926
• Languages: English
• ISSNs: 0965-9978

Abstract

Highlights: New parallel implementation strategy for meshless methods. Performance portability in different architectures. Definition of the physical-mathematical model by declarative programming. Numerical results show that the proposed strategy is accurate and competitiveness. Abstract: In this paper a parallel implementation strategy is presented, for meshless methods, using principles of functional programming and memory polymorphism. The meta-programming presented technique has been developed to guarantee portability of performance in different execution spaces and simplified representation of the mathematical physics model through simple use of declarative programming. To this end, an execution model for meshless methods is defined, under the premise that specializations for execution and memory spaces must take place during compilation time. This means that, the declared model can be simulated in different architectures, without any changes in the implementation. In addition, the included results demonstrate that, it is possible to guarantee portability of performance while reducing the complexity of the model representation. In order to illustrate the defined strategy, an application using the so called meshless local Petrov-Galerkin (MLPG) method is presented. Results are discussed for different boundary value problems, indicating that the presented procedure succeeds in reducing implementation costs to just the time to adapt its efficiency to the execution space of the simulation.