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DAG-Guided Parallel Asynchronous Variational Integrators with Super-Elements

机译:具有超级元素的DAG引导并行异步变分集成分程

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Physical systems often involve a wide spectrum of time scales, making traditional synchronous time integrators too slow even on the world's fastest supercomputers. Asynchronous variational integrators (AVIs) have recently been introduced to address this challenge for a class of nonlinear elastodynamic problems. In this paper, we introduce a new and efficient parallel algorithm for the AVIs. We propose a new local-minima interpretation of the dependencies in the AVIs. Based on this interpretation, we show that the expected amount of parallelism in the AVIs is proportional to the number of elements of a mesh assuming a uniform distribution of the dependencies. Second, based on the localminima interpretation, we introduce a parallel algorithm for the AVIs guided by the dependency graph of the computation that avoids using priority queues, thereby improving scalability. Third, we present an efficient multi-threaded implementation of the AVIs and introduce performance-optimization techniques using super-elements. Preliminary experimental results are presented to verify our theoretical analysis and demonstrate the effectiveness of the proposed algorithms.
机译:物理系统通常涉及广泛的时间尺度,即使在世界上最快的超级计算机上,传统的同步时间集成商也太慢了。最近介绍了异步变分集成商(AVIS)以解决一类非线性弹性动力学问题的这一挑战。在本文中,我们为AVI介绍了一种新的有效的并行算法。我们提出了一种新的本地最小值解释AVIS中的依赖关系。基于此解释,我们表明AVIS中的预期并行量与假设依赖关系的统一分布成均匀分布的网格的元素数量成比例。其次,基于LocalMinima解释,我们向AVI引入了由计算的依赖性图的平行算法,避免使用优先级队列,从而提高可伸缩性。第三,我们介绍了AVIS的有效多线程实现,并使用超元素引入性能优化技术。提出了初步实验结果以验证我们的理论分析并证明所提出的算法的有效性。

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