Meshfree methods such as the Reproducing Kernel Particle Method and the Element Free Galerkin method have proven to be excellent choices for problems involving complex geometry, evolving topology, and large deformatio...
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Meshfree methods such as the Reproducing Kernel Particle Method and the Element Free Galerkin method have proven to be excellent choices for problems involving complex geometry, evolving topology, and large deformation, owing to their ability to model the problem domain without the constraints imposed on the Finite Element Method (FEM) meshes. However, meshfree methods have an added computational cost over FEM that come from at least two sources: increased cost of shape function evaluation and the determination of adjacency or connectivity. The focus of this paper is to formally address the types of adjacency information that arises in various uses of meshfree methods;a discussion of available techniques for computing the various adjacency graphs;propose a new search algorithm and datastructure;and finally compare the memory and run time performance of the methods.
3D particle data is relevant for a wide range of scientific domains, from molecular dynamics to astrophysics. Simulations in these domains can produce datasets containing millions or billions of particles and renderin...
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ISBN:
(纸本)9781728184685
3D particle data is relevant for a wide range of scientific domains, from molecular dynamics to astrophysics. Simulations in these domains can produce datasets containing millions or billions of particles and rendering needs to be in high quality and interactive to support the scientists in exploring and understanding the structure of their data. One general baseline approach is to represent particles as spheres and employ ray tracing as a rendering technique. However, ray tracing requires the data to be organized in acceleration data structures like bounding volume hierarchies (BVH) to achieve interactive frame rates. Modern GPUs provide hardware acceleration for traversing such datastructures but are more limited in memory than CPUs. In this paper, we evaluate different acceleration data structures for sphere-based datasets, including particle kD trees, with respect to their scalability regarding both memory size and speed, and we analyze how these datastructures can benefit from hardware acceleration. We show that a bricking of data results in the most effective BVH, both fast to traverse utilizing hardware acceleration and with a reasonably small memory footprint. Additionally, we present a hybrid acceleration data structure that has negligible memory overhead and still ensures reasonable traversal speed. Based on our results, visualization tools and APIs for the ray tracing can provide overall better performance by adapting to the needs of particle-centric application scenarios.
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