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.

在本文中，针对无网格方法，使用函数式编程和内存多态性的原理，提出了一种并行实现策略。通过开发元编程提出的技术，可以保证在不同执行空间中性能的可移植性，并可以通过简单地使用声明式编程来简化数学物理模型的表示。为此，在必须在编译期间对执行和存储空间进行专门化的前提下，定义了无网格方法的执行模型。这意味着，可以在不同的体系结构中对声明的模型进行仿真，而无需在实现中进行任何更改。此外，所包含的结果表明，可以在保证性能可移植性的同时降低模型表示的复杂性。为了说明定义的策略，提出了使用所谓的无网格局部Petrov-Galerkin（MLPG）方法的应用程序。讨论了针对不同边值问题的结果，表明所提出的过程成功地将实施成本降低到了恰好可以使其效率适应仿真执行空间的时间。