Accelerating Force-Directed Graph Drawing with RT Cores

Zellmann, Stefan; Weier, Martin; Wald, Ingo

doi:10.1109/vis47514.2020.00026

Cited by 18 publications

(9 citation statements)

References 29 publications

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“…The only requirement is that one can reformulate their algorithms to be mapped to the ray tracing hardware. For example, Zellmann et al [1] used RT cores to accelerate force-directed graph drawing, where the efficacy of spring forces decreases with distance. The computations involved can be accelerated using a search tree over the graph's vertex set.…”

Section: Department Headmentioning

confidence: 99%

“…For example, the problem domain must be embeddable in R 3 , and in general, the problem must be mappable to a ray tracing problem without introducing significant overhead. In the previous example of the radius point queries [1], the authors observed that a fixed radius point query over a set of particles could be reformulated as a ray-tracing problem by using Ray-traced point containment queries. For simplicity, the integration domain here is deliberately just a single box, but would typically be comprised of multiple overlapping finite elements or grid cells.…”

Section: Department Headmentioning

confidence: 99%

“…Point containment queries with RT cores have been proposed by several researchers, including the aforementioned force-directed graph drawing algorithm by Zellmann et al [1]. Even more closely related to our approach is the work by Morrical et al [3] who used point containment queries to evaluate the particle density at arbitrary sampling positions x ∈ R 3 given an underlying unstructured grid representation with finite ele-ments such as tetrahedra or hexahedra.…”

Section: Point Containment Queries With Nvidia Rtx and Optixmentioning

confidence: 99%

“…The description and terminology we use roughly follows the OptiX API. 1 OptiX exposes entry points for the programmer to initiate and control ray generation and ray/object interactions. The ray generation program is comparable in nature to a compute kernel as it is executed by the threads of the programmable shading multiprocessors.…”

Section: Point Containment Queries With Nvidia Rtx and Optixmentioning

confidence: 99%

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Point Containment Queries on Ray-Tracing Cores for AMR Flow Visualization

et al. 2022

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Modern GPUs come with dedicated hardware to perform ray/triangle intersections and bounding volume hierarchy (BVH) traversal. While the primary use case for this hardware is photorealistic 3D computer graphics, with careful algorithm design scientists can also use this special-purpose hardware to accelerate general-purpose computations such as point containment queries. This article explains the principles behind these techniques and their application to vector field visualization of large simulation data using particle tracing.RAY TRACING is a computer graphics algorithm that is based on geometric optics. While traditionally being focused on offline production rendering by the film industry, the recent addition IT Professional

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Section: Department Headmentioning

confidence: 99%

Section: Department Headmentioning

confidence: 99%

Section: Point Containment Queries With Nvidia Rtx and Optixmentioning

confidence: 99%

Section: Point Containment Queries With Nvidia Rtx and Optixmentioning

confidence: 99%

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Point Containment Queries on Ray-Tracing Cores for AMR Flow Visualization

et al. 2022

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“…The Facebook friendship graph is commonly evaluated on the link prediction task. The largest graph, a Twitter feed graph, has been used in the experiments by Zellmann et al 10 and provided to us by the authors.…”

Section: Datasetsmentioning

confidence: 99%

Evaluating Representation Learning and Graph Layout Methods for Visualization

Heiter

Kang

Bie

et al. 2022

IEEE Comput. Grap. Appl.

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Leveraging ray tracing cores for particle‐based simulations on GPUs

Zhao

Lai

Zhao

2022

Numerical Meth Engineering

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Neighbor searching is an essential and computationally heavy step in particle‐based numerical methods such as discrete element method (DEM), molecular dynamics, peridynamics, and smooth particle hydrodynamics. This article presents a novel approach to accelerate particle‐based simulations by leveraging ray tracing (RT) cores in addition to CUDA cores on RTX GPUs. The neighbor search problem is first numerically converted into a general ray tracing problem so that it can be possible to utilize the hardware acceleration of RT cores. A new, general‐purpose RT‐based neighbor search algorithm is then proposed and benchmarked with a prevailing cell‐based one. As a showcase, both algorithms are implemented into a GPU‐based DEM code for simulating large‐scale granular problems including packing, column collapse and debris flow. The overall simulation performance is examined with varying problem sizes and GPU specs. It demonstrates that the RT‐based simulations are 10%–60% faster than the cell‐based ones, depending on the simulated problems and GPU specs. This study offers a new recipe for next‐generation high‐performance computing of large‐scale engineering problems using particle‐based numerical methods.

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Accelerating Force-Directed Graph Drawing with RT Cores

Cited by 18 publications

References 29 publications

Point Containment Queries on Ray-Tracing Cores for AMR Flow Visualization

Point Containment Queries on Ray-Tracing Cores for AMR Flow Visualization

Evaluating Representation Learning and Graph Layout Methods for Visualization

Leveraging ray tracing cores for particle‐based simulations on GPUs

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