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High-performance simulations of turbulent boundary layer flow using Intel Xeon Phi many-core processors

机译:Intel Xeon Phi许多核心处理器的湍流边界层流量高性能模拟

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Direct numerical simulations (DNS) of turbulent flows have increasing importance because they not only provide fundamental understanding of turbulent flows but also complement and extend experimental results. DNS of high Reynolds numbers, however, require huge computing cost so high-performance computing has been strongly pursued. In this study, we examine the feasibility of cost-efficient DNS on Intel Xeon Phi many-core processors that are currently adopted by 10% of the 100 largest supercomputers in the world as listed in the Top500 site. For this purpose, we port and optimize our in-house turbulent flow solver named as DNS-TBL (direct numerical simulation-turbulent boundary layer) on Xeon Phi Knights Landing (KNL) many-core processors and conduct benchmark tests on KNL and conventional multicore processors. The key architectural features of KNL processors and strategies to exploit them for performance enhancement are discussed. The optimized code is validated by conducting numerical simulations of zero-pressure gradient turbulent boundary layers at high Reynolds numbers and by comparing simulated turbulent statistics to those reported in previous studies. With the details of optimization strategies and validation processes, this work can serve as a practical guideline for acceleration of large-scale and precise DNS with many-core computing.
机译:湍流流动的直接数值模拟(DNS)具有越来越重要的,因为它们不仅为对湍流进行的根本理解,而且补充并延长实验结果。然而,高雷诺数的DNS需要巨大的计算成本,因此强烈追求高性能计算。在这项研究中,我们研究了Intel Xeon Phi多核处理器上的成本效益DNS的可行性,目前在Top500网站上市的世界上100%的全球100个最大超级计算机的10%采用。为此目的,我们在Xeon Phi Knights Landing(KNL)许多核心处理器上,我们的内部湍流求解器命名为DNS-TBL(直接数值模拟湍流边界层),并在KNL和传统多核上进行基准测试处理器。讨论了KNL处理器和策略利用它们进行性能增强的关键架构特征。通过在高雷诺数的零压梯度湍流边界层进行数值模拟并通过将模拟湍流统计数据与先前研究报告的那些进行比较来验证优化的代码。通过优化策略和验证流程的详细信息,这项工作可以作为加速大规模和精确DNS的实用指南,并具有许多核心计算。

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