Towards Green Aviation with Python at Petascale
Towards Green Aviation with Python at Petascale
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DOI：
10.1109/sc.2016.1
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发表时间：
2016-11
期刊：
影响因子：
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通讯作者：
P. Vincent;F. Witherden;Brian C. Vermeire;J. Park;A. Iyer
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文献类型：
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作者：
P. Vincent;F. Witherden;Brian C. Vermeire;J. Park;A. Iyer
非定常湍流的精确模拟对于改进更安静、更省油的绿色飞机的设计至关重要。我们演示了PyFR，基于Python的计算流体动力学求解器，petascale模拟这样的流动问题的应用。算法选择背后的原理，它提供了更高的精度水平，并使持续计算高达58%的峰值DP-FLOP/s的非结构化网格，将在现代硬件的背景下进行讨论。还将详细介绍一系列软件创新，包括使用运行时代码生成，这使得PyFR能够通过单个实现有效地针对多个平台，包括异构系统。最后，将展示低压涡轮机叶片叶栅流动的全尺寸模拟结果，沿着Piz Daint和Titan超级计算机的弱/强缩放统计数据，以及表明可持续计算的性能数据高达13.7 DP-PFLOP/s。
Accurate simulation of unsteady turbulent flow is critical for improved design of greener aircraft that are quieter and more fuel-efficient. We demonstrate application of PyFR, a Python based computational fluid dynamics solver, to petascale simulation of such flow problems. Rationale behind algorithmic choices, which offer increased levels of accuracy and enable sustained computation at up to 58% of peak DP-FLOP/s on unstructured grids, will be discussed in the context of modern hardware. A range of software innovations will also be detailed, including use of runtime code generation, which enables PyFR to efficiently target multiple platforms, including heterogeneous systems, via a single implementation. Finally, results will be presented from a fullscale simulation of flow over a low-pressure turbine blade cascade, along with weak/strong scaling statistics from the Piz Daint and Titan supercomputers, and performance data demonstrating sustained computation at up to 13.7 DP-PFLOP/s.