![]() Moreover, we can easily scale the cylinder to make it larger or smaller all we need to do is maintain the ratio of the length to the radius. In terms of the geometry, this information is all we need in order to render the cylinder in a 3D scene. A cylinder can be defined in very simple mathematical terms: all you really need to know is the radius at the ends and the length of the cylinder. Using this method, the level of detail is (practically speaking) "infinite". The first is one you don't see a lot of in games, and it involves using precise, mathematically defined curves to define the shape of an object. There are basically two ways to model 3D objects. ![]() This performance is 2.7 times more memory efficient, 9 times more scalable and 86 times faster than the state-of-the-art algorithm.I'll give you the "simple" version and let someone else fill you in on the details if you're interested :). ![]() In addition, it achieves time-to-solution within 2.2 minutes by scaling to 4158 GPUs with a super-linear strong scaling efficiency at 364% compared to runtimes at 6 GPUs. Our experiments on a Titanate material dataset (PbTiO3) with 16632 probe locations show that our Gradient Decomposition algorithm reduces memory footprint by 51 times. In addition, we propose a parallel image gradient decomposition method that enables asynchronous point-to-point communications and parallel more » pipelining with minimal overhead on a large number of GPUs. In this paper, we propose a novel image gradient decomposition method that significantly reduces the memory footprint for ptychographic reconstruction by tessellating image gradients and diffraction measurements into tiles. Unfortunately, the high image resolution for ptychographic reconstruction requires significant amount of memory and computations, forcing many applications to compromise their image resolution in exchange for a smaller memory footprint and a shorter reconstruction time. Ptychography is a popular microscopic imaging modality for many scientific discoveries and sets the record for highest image resolution. Moreover, in the unbalanced data sets, decomposing the domain into a k-d tree is up to five times faster than decomposing it into a regular grid. The new running times are up to 50 times faster using k-d tree compared with regular grid decomposition. We evaluate more » the new algorithm using two late-stage cosmology datasets. Because resulting point distributions no longer satisfy the assumptions of existing parallel Delaunay algorithms, we develop a new parallel algorithm that adapts to its input and prove its correctness. We investigate the use of k-d trees to evenly distribute points among processes and compare two strategies for picking split points between domain regions. The algorithms for computing these tessellations at scale perform poorly when the input data is unbalanced. They are important in data analysis, where they can represent the geometry of a point set or approximate its density. ![]() ![]() « lessĭelaunay tessellations are fundamental data structures in computational geometry. We illustrate our method with several toy examples of both straight and curved boundaries with varying amounts of signal present in the data. Here we propose an alternative approach where we simultaneously form and evaluate the significance of all possible boundaries in terms of the total gradient flux. The outcome from traditional wombling algorithms is a set of boundary cell candidates with relatively large gradients, whose spatial properties must then more » be scrutinized in order to construct the boundary and evaluate its significance. We discuss the use of Voronoi and Delaunay tessellations of the point data for estimating the local gradients and investigate methods for sharpening the boundaries by reducing the statistical noise. We address the problem of finding a wombling boundary in point data generated by a general Poisson point process, a specific example of which is an LHC event sample distributed in the phase space of a final state signature, with the wombling boundary created by some new physics. ![]()
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