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Parallel Particle Advection and FTLE Computation for Time-Varying Flow Fields

机译:平行粒子平流和时变流场的特殊计算

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Flow fields are an important product of scientific simulations. One popular flow visualization technique is particle advection, in which seeds are traced through the flow field. One use of these traces is to compute a powerful analysis tool called the Finite-Time Lyapunov Exponent (FTLE) field, but no existing particle tracing algorithms scale to the particle injection frequency required for high-resolution FTLE analysis. In this paper, a framework to trace the massive number of particles necessary for FTLE computation is presented. A new approach is explored, in which processes are divided into groups, and are responsible for mutually exclusive spans of time. This pipelining over time intervals reduces overall idle time of processes and decreases I/O overhead. Our parallel FTLE framework is capable of advecting hundreds of millions of particles at once, with performance scaling up to tens of thousands of processes.
机译:流场是科学模拟的重要产品。一种流行的流动可视化技术是粒子平流,其中通过流场跟踪种子。这些迹线的一次使用是计算一个强大的分析工具,称为有限时间Lyapunov指数(FTLE)字段,但是对于高分辨率分析所需的颗粒注射频率没有现有的粒子跟踪算法。在本文中,呈现了一种追踪质量计算所需的大量粒子的框架。探索了一种新方法,其中流程分为组,并负责相互排斥的时间。这种流水线随着时间的间隔减少了过程的整体空闲时间并减少了I / O开销。我们的平行质量框架能够立即加速数亿颗粒,性能缩放到数万个过程。

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