Based on trace theory, we study efficient methods for concurrent integration of B-spline basis functions in IGA-FEM. We consider several scenarios of parallelization for two standard integration methods; the classical one and sum factorization. We aim to efficiently utilize hybrid memory machines, such as modern clusters, by focusing on the non-obvious layer of the shared memory part of concurrency. We estimate the performance of computations on a GPU and provide a strategy for performing such computations in practical implementations.
翻译:根据追踪理论,我们研究在IGA-FEM同时整合B-Spline基础功能的有效方法,我们考虑两种标准集成方法的几种平行方案:古典集成法和总因子化法;我们的目标是有效利用混合记忆机,例如现代集成法,侧重于共同货币共同记忆部分的非明显层;我们估计GPU的计算工作绩效,并提供在实际执行中进行这种计算的战略。