Interactive terascale particle visualization

Ellsworth, David; Green, Bryan; Moran, Patrick

doi:10.1109/visual.2004.55

Cited by 26 publications

(13 citation statements)

References 10 publications

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“…In previous work it has also been proposed to precompute and store particle trajectories for a number of prescribed seed points, and to restrict the visualization to subsets of these trajectories (Lane, 1994;Bruckschen et al, 2001;Ellsworth et al, 2004). In this way, all computation is shifted to the preprocessing stage, and storage as well as bandwidth limitations at runtime can be overcome.…”

Section: Related Workmentioning

confidence: 99%

Analyzing the Effect of Lossy Compression on Particle Traces in Turbulent Vector Fields

Treib

Burger

et al. 2015

Proceedings of the 6th International Conference on Information Visualization Theory and Applications

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Abstract:We shed light on the accuracy of particle trajectories in turbulent vector fields when lossy data compression is used. So far, data compression has been considered rather hesitantly due to supposed accuracy issues. Motivated by the observation that particle traces are always afflicted with inaccuracy, we quantitatively analyze the additional inaccuracies caused by lossy compression. In some experiments we confirm that the compression has only minor impact on the accuracy of the trajectories. Even though our experiments are not generally valid, they indicate that a more thorough analysis of the error in particle integration due to compression is necessary, and that in some cases lossy compression is valid and can significantly reduce performance limitations due to memory and communication bandwidth.

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Section: Related Workmentioning

confidence: 99%

Analyzing the Effect of Lossy Compression on Particle Traces in Turbulent Vector Fields

Treib

Burger

et al. 2015

Proceedings of the 6th International Conference on Information Visualization Theory and Applications

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“…In turbulent flow fields, however, using a lower-resolution approximation of the velocity data results in a significant distortion of the extracted features and is thus not admissible. Specifically for large flow data, Ellsworth et al [7] present a particle-based visualization system that precomputes a large number of particle traces which can then be displayed interactively.…”

Section: Related Workmentioning

confidence: 99%

Turbulence Visualization at the Terascale on Desktop PCs

Treib

Burger

Reichl

et al. 2012

IEEE Trans. Visual. Comput. Graphics

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Fig. 1. Visualizations of structures in 1024 3 turbulence data sets on 1024 × 1024 viewports, directly from the turbulent motion field. Left: Close-up of iso-surfaces of the ∆ Chong invariant with direct volume rendering of vorticity direction inside the vortex tubes. Middle: Direct volume rendering of color-coded vorticity direction. Right: Close-up of direct volume rendering of R S . The visualizations are generated by our system in less than 5 seconds on a desktop PC equipped with 12 GB of main memory and an NVIDIA GeForce GTX 580 graphics card with 1.5 GB of video memory.Abstract-Despite the ongoing efforts in turbulence research, the universal properties of the turbulence small-scale structure and the relationships between small-and large-scale turbulent motions are not yet fully understood. The visually guided exploration of turbulence features, including the interactive selection and simultaneous visualization of multiple features, can further progress our understanding of turbulence. Accomplishing this task for flow fields in which the full turbulence spectrum is well resolved is challenging on desktop computers. This is due to the extreme resolution of such fields, requiring memory and bandwidth capacities going beyond what is currently available. To overcome these limitations, we present a GPU system for feature-based turbulence visualization that works on a compressed flow field representation. We use a wavelet-based compression scheme including run-length and entropy encoding, which can be decoded on the GPU and embedded into brick-based volume ray-casting. This enables a drastic reduction of the data to be streamed from disk to GPU memory. Our system derives turbulence properties directly from the velocity gradient tensor, and it either renders these properties in turn or generates and renders scalar feature volumes. The quality and efficiency of the system is demonstrated in the visualization of two unsteady turbulence simulations, each comprising a spatio-temporal resolution of 1024 4 . On a desktop computer, the system can visualize each time step in 5 seconds, and it achieves about three times this rate for the visualization of a scalar feature volume.

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“…Other examples of applying parallel computation to integral curve-based visualization include the use of multiprocessor workstations to parallelize integral curve computation (e.g., [14]), and research efforts focusing on accelerating specific visualization techniques [4]. Similarly, PC cluster systems were leveraged to accelerate advanced integration-based visualization algorithms, such as time-varying Line Integral Convolution (LIC) volumes [16] or particle visualization for very large data [7].…”

Section: Parallel Considerationsmentioning

confidence: 99%

Parallel stream surface computation for large data sets

Camp

Childs

Garth

et al. 2012

IEEE Symposium on Large Data Analysis and Visualization (LDAV)

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Figure 1: Stream surfaces generated during this study. On the left, flow around an ellipsoid. In the middle, flow in a tokamak. On the right, flow from a thermal hydraulics simulation. ABSTRACTParallel stream surface calculation, while highly related to other particle advection-based techniques such as streamlines, has its own unique characteristics that merit independent study. Specifically, stream surfaces require new integral curves to be added continuously during execution to ensure surface quality and accuracy; performance can be improved by specifically accounting for these additional particles. We present an algorithm for generating stream surfaces in a distributed-memory parallel setting. The algorithm incorporates multiple schemes for parallelizing particle advection and we study which schemes work best. Further, we explore speculative calculation and how it can improve overall performance. In total, this study informs the efficient calculation of stream surfaces in parallel for large data sets, based on existing integral curve functionality.

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Interactive terascale particle visualization

Cited by 26 publications

References 10 publications

Analyzing the Effect of Lossy Compression on Particle Traces in Turbulent Vector Fields

Analyzing the Effect of Lossy Compression on Particle Traces in Turbulent Vector Fields

Turbulence Visualization at the Terascale on Desktop PCs

Parallel stream surface computation for large data sets

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