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Interactive flow simulation using Tegra-powered mobile devices

机译:使用Tegra驱动的移动设备进行交互式流动模拟

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摘要

The ability to perform interactive CFD simulations on mobile devices allows the development of portable, affordable simulation tools that can have a significant impact in engineering design as well as teaching and learning. This work extends existing work in the area by developing and implementing a GPU-accelerated, interactive simulation framework suitable for mobile devices. The accuracy, throughput, memory usage and battery consumption of the application is established for a range of problem sizes. The current GPU implementation is found to be over 300 more efficient in terms of combined throughput and power consumption than a comparable CPU implementation. The usability of the simulation is examined through a new ‘interactivity’ metric which identifies the ratio of simulated convection to real world convection of the same problem. This real-time ratio illustrates that large resolutions may increase throughput through parallelisation on the GPU but this only partially offsets the decrease in simulated flow rate due to the necessary shrinking of the time step in the solver with increasing resolution. Therefore, targeting higher throughput configurations of GPU-solvers offer little additional benefit for interactive applications due to ultimately simulations evolving at a too slow a rate to facilitate interaction. The trade-off between accuracy, speed and power consumption are explored with the choice of problem resolution ultimately being characterised by a desired accuracy, flow speed and endurance of a given simulation. With current rates of growth in mobile compute power expected to continue, real-time simulation is expected to be possible at higher resolutionswith a reduced energy footprint in the near future.
机译:通过在移动设备上执行交互式CFD仿真的能力,可以开发出价格合理的便携式仿真工具,这些工具可能会对工程设计以及教学产生重大影响。通过开发和实现适用于移动设备的GPU加速的交互式仿真框架,这项工作扩展了该领域的现有工作。应用程序的准确性,吞吐量,内存使用情况和电池消耗是针对一系列问题大小确定的。发现当前的GPU实现在吞吐量和功耗方面要比同类CPU实现高300倍。通过新的“交互性”度量来检查模拟的可用性,该度量可确定相同问题的模拟对流与现实世界对流的比率。此实时比率说明,大分辨率可以通过GPU上的并行化来提高吞吐量,但这仅部分抵消了模拟流速的降低,这是因为随着分辨率的提高,求解器中时间步长的必要缩减。因此,针对GPU求解器的更高吞吐量配置,由于最终模拟以太慢的速度发展而无法促进交互,因此对于交互式应用程序几乎没有其他好处。探索了精度,速度和功耗之间的权衡,选择问题的方法最终以给定仿真的所需精度,流速和耐久性为特征。随着当前移动计算能力的增长速度预计将继续,在不久的将来,有望以更高分辨率实现实时仿真并减少能耗。

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