Extreme-Scale Stochastic Particle Tracing for Uncertain Unsteady Flow Visualization and Analysis
Extreme-Scale Stochastic Particle Tracing for Uncertain Unsteady Flow Visualization and Analysis
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DOI：
10.1109/tvcg.2018.2856772
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发表时间：
2019-09
影响因子：
5.2
通讯作者：
Hanqi Guo;Wenbin He;Sangmin Seo;Han-Wei Shen;E. Constantinescu;Chunhui Liu;T. Peterka
中科院分区：
文献类型：
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作者：
Hanqi Guo;Wenbin He;Sangmin Seo;Han-Wei Shen;E. Constantinescu;Chunhui Liu;T. Peterka
我们提出了一个有效的和可扩展的解决方案来估计不确定的运输行为-随机流图（SFM）-可视化和分析不确定的不稳定流。从不确定的流场计算流图是非常昂贵的，因为它需要许多蒙特卡罗运行来跟踪流中密集的种子粒子。我们通过解耦SFM中的时间依赖性来降低计算成本，以便我们可以独立地处理较短的子时间间隔，然后将它们组合在一起以获得更长的时间段。自适应细化还用于减少每个位置的运行次数。我们并行化的任务包的粒子在我们的设计，以实现高效率的MPI/线程混合编程。这样的任务模型还支持CPU/GPU协处理。我们展示了两台超级计算机的可扩展性，Mira（高达256 K Blue Gene/Q内核）和Titan（高达128 K Opteron内核和8 K GPU），可以在几秒钟内追踪数十亿个粒子。
We present an efficient and scalable solution to estimate uncertain transport behaviors—stochastic flow maps (SFMs)—for visualizing and analyzing uncertain unsteady flows. Computing flow maps from uncertain flow fields is extremely expensive because it requires many Monte Carlo runs to trace densely seeded particles in the flow. We reduce the computational cost by decoupling the time dependencies in SFMs so that we can process shorter sub time intervals independently and then compose them together for longer time periods. Adaptive refinement is also used to reduce the number of runs for each location. We parallelize over tasks—packets of particles in our design—to achieve high efficiency in MPI/thread hybrid programming. Such a task model also enables CPU/GPU coprocessing. We show the scalability on two supercomputers, Mira (up to 256K Blue Gene/Q cores) and Titan (up to 128K Opteron cores and 8K GPUs), that can trace billions of particles in seconds.