可缩放的现场拉格朗日流图提取：演示无通信模型的可行性
Scalable In Situ Lagrangian Flow Map Extraction: Demonstrating the Viability of a Communication-Free Model
摘要：
我们介绍和评估一种新的算法，在现场提取拉格朗日流图，我们称之为边界终止优化（BTO）。我们的方法是一个无通信的模型，不需要消息传递或进程之间的同步，提高可扩展性，从而减少整体执行时间，并减轻在原位处理的模拟代码上放置的障碍。我们在节点边界处终止粒子集成，并仅存储通过跨节点通信粒子而提取的流图的子集，从而引入了准确性-性能权衡。我们运行实验多达2048 GPU和多个模拟数据集。对于我们考虑的实验配置，我们的研究结果表明，我们的无通信技术在原位节省了2倍至4倍的执行时间，同时在定量和定性上与以前的工作几乎一样准确。最重要的是，这项研究建立了可行性接近在原位拉格朗日流图提取使用无通信模型在未来。
英文摘要：
We introduce and evaluate a new algorithm for the in situ extraction of Lagrangian flow maps, which we call Boundary Termination Optimization (BTO). Our approach is a communication-free model, requiring no message passing or synchronization between processes, improving scalability, thereby reducing overall execution time and alleviating the encumbrance placed on simulation codes from in situ processing. We terminate particle integration at node boundaries and store only a subset of the flow map that would have been extracted by communicating particles across nodes, thus introducing an accuracy-performance tradeoff. We run experiments with as many as 2048 GPUs and with multiple simulation data sets. For the experiment configurations we consider, our findings demonstrate that our communication-free technique saves as much as 2x to 4x in execution time in situ, while staying nearly as accurate quantitatively and qualitatively as previous work. Most significantly, this study establishes the viability of approaching in situ Lagrangian flow map extraction using communication-free models in the future.
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