.png){kind=icon}
.png){kind=icon}
Return
A parallel implementation strategy for meshless methods based on the functional programming paradigm
.png){kind=icon}
PRE
AI
DOI：10.1016/j.advengsoft.2020.102926.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
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.
Keywords:
Meshless method
Functional programming
Massively parallel programming
Meshless local Ppetrov-Galerkin (MLPG) method
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
5.7
Papers:
3.3K
Citations:
1.2W