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Spoke-darts for high-dimensional blue-noise sampling

ACM Transactions on Graphics

Mitchell, Scott A.; Ebeida, Mohamed S.; Awad, Muhammad A.; Park, Chonhyon; Patney, Anjul; Rushdi, Ahmad A.; Swiler, Laura P.; Manocha, Dinesh; Wei, Li Y.

Blue noise sampling has proved useful for many graphics applications, but remains underexplored in high-dimensional spaces due to the difficulty of generating distributions and proving properties about them. We present a blue noise sampling method with good quality and performance across different dimensions. The method, spoke-dart sampling, shoots rays from prior samples and selects samples from these rays. It combines the advantages of two major high-dimensional sampling methods: the locality of advancing front with the dimensionality-reduction of hyperplanes, specifically line sampling. We prove that the output sampling is saturated with high probability, with bounds on distances between pairs of samples and between any domain point and its nearest sample. We demonstrate spoke-dart applications for approximate Delaunay graph construction, global optimization, and robotic motion planning. Both the blue-noise quality of the output distribution and the adaptability of the intermediate processes of our method are useful in these applications.

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TYPE Conference Poster YEAR 2018
DOIOSTIScopus

Deploy threading in Nalu solver stack

Prokopenko, Andrey; Thomas, Stephen; Swirydowicz, Kasia; Ananthan, Shreyas; Hu, Jonathan J.; Williams, Alan B.; Sprague, Michael

The goal of the ExaWind project is to enable predictive simulations of wind farms composed of many MW-scale turbines situated in complex terrain. Predictive simulations will require computational fluid dynamics (CFD) simulations for which the mesh resolves the geometry of the turbines, and captures the rotation and large deflections of blades. Whereas such simulations for a single turbine are arguably petascale class, multi-turbine wind farm simulations will require exascale-class resources. The primary code in the ExaWind project is Nalu, which is an unstructured-grid solver for the acoustically-incompressible Navier-Stokes equations, and mass continuity is maintained through pressure projection. The model consists of the mass-continuity Poisson-type equation for pressure and a momentum equation for the velocity. For such modeling approaches, simulation times are dominated by linear-system setup and solution for the continuity and momentum systems. For the ExaWind challenge problem, the moving meshes greatly affect overall solver costs as re-initialization of matrices and re-computation of preconditioners is required at every time step.

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TYPE Other Report YEAR 2018
DOIOSTI
Results 3026–3050 of 9,998
Results 3026–3050 of 9,998
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