Particle tracing in fully resolved turbulent vector fields is challenging due to their extreme resolution. Since particles can move along arbitrary paths through large parts of the domain, particle integration requires access to the entire field in an unpredictable order. Thus, techniques for particle tracing in such fields require a careful design to reduce performance constraints caused by memory and communication bandwidth. One possibility to achieve this is data compression, but so far it has been considered rather hesitantly due to supposed accuracy issues. We shed light on the use of data compression for turbulent vector fields, motivated by the observation that particle traces are always afflicted with inaccuracy. We quantitatively analyze the additional inaccuracies caused by lossy compression. We propose an adaptive data compression scheme using the discrete wavelet transform and integrate it into a block-based particle tracing approach. Furthermore, we present a priority-based GPU caching scheme to reduce memory access operations. In some experiments we confirm that the compression has only minor impact on the accuracy of the trajectories, and that on a desktop system our technique can achieve comparable performance to previous approaches on supercomputers.

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.

Interactive and high-quality visualization of spatially continuous 3D fields represented by scattered distributions of billions of particles is challenging. One common approach is to resample the quantities carried by the particles to a regular grid and to render the grid via volume ray-casting. In large-scale applications such as astrophysics, however, the required grid resolution can easily exceed 10K samples per spatial dimension, letting resampling approaches appear unfeasible. In this paper we demonstrate that even in these extreme cases such approaches perform surprisingly well, both in terms of memory requirement and rendering performance. We resample the particle data to a multiresolution multiblock grid, where the resolution of the blocks is dictated by the particle distribution. From this structure we build an octree grid, and we then compress each block in the hierarchy at no visual loss using wavelet-based compression. Since decompression can be performed on the GPU, it can be integrated effectively into GPU-based out-of-core volume ray-casting. We compare our approach to the perspective grid approach which resamples at run-time into a view-aligned grid. We demonstrate considerably faster rendering times at high quality, at only a moderate memory increase compared to the raw particle set.

Flow simulations around building infrastructure models involve large scale complex geometries, which when discretized in adequate detail entail high computational cost. Moreover, tasks such as simulation insight by steering or optimization require many such costly simulations. In this paper, we illustrate the whole pipeline of an integrated solution for interactive computational steering, developed for complex flow simulation scenarios that depend on a moderate number of both geometric and physical parameters. A mesh generator takes building information model input data and outputs a valid cartesian discretization. A sparse-grids-based surrogate model—a less costly substitute for the parameterized simulation—uses precomputed data to deliver approximated simulation results at interactive rates. Furthermore, a distributed multi-display visualization environment shows building infrastructure together with flow data. The focus is set on scalability and intuitive user interaction.

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 10244. 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.

The JHU turbulence database [1] can be used with a state of the art visualisation tool [2] to generate high quality fluid dynamics videos. In this work we investigate the classical idea that smaller structures in turbulent flows, while engaged in their own internal dynamics, are advected by the larger structures. They are not advected undistorted, however. We see instead that the small scale structures are sheared and twisted by the larger scales. This illuminates the basic mechanisms of the turbulent cascade.

The Closest Point Method (CPM) is a method for numerically solving partial differential equations (PDEs) on arbitrary surfaces, independent of the existence of a surface parametrization. The CPM uses a closest point representation of the surface, to solve the unmodified Cartesian version of a surface PDE in a 3D volume embedding, using simple and well‐understood techniques. In this paper, we present the numerical solution of the wave equation and the incompressible Navier‐Stokes equations on surfaces via the CPM, and we demonstrate surface appearance and shape variations in real‐time using this method. To fully exploit the potential of the CPM, we present a novel GPU realization of the entire CPM pipeline. We propose a surface‐embedding adaptive 3D spatial grid for efficient representation of the surface, and present a high‐performance approach using CUDA for converting surfaces given by triangulations into this representation. For real‐time performance, CUDA is also used for the numerical procedures of the CPM. For rendering the surface (and the PDE solution) directly from the closest point representation without the need to reconstruct a triangulated surface, we present a GPU ray‐casting method that works on the adaptive 3D grid.

The augmentation of objects by textual annotations provides a powerful means for visual data exploration. Especially in interactive scenarios, where the view on the objects and, thus, the preferred placement of annotations changes continually, efficient labeling procedures are required. As identified by a preliminary study for this paper, these procedures have to consider a number of requirements for achieving an optimal readability, e.g. cartographic principles, visual association and temporal coherence. In this paper, we present a force-based labeling algorithm for 2D and 3D scenes, which can compute the placements of annotations at very high speed and fulfills the identified requirements. The efficient labeling of several hundred annotations is achieved by computing their layout in parallel on the GPU. This allows for a real-time and collision-free arrangement of both dynamically changing and static information. We demonstrate that our method supports a large variety of applications, e.g. geographical information systems, automotive navigation systems, and scientific or information visualization systems. We conclude the paper with an expert study which confirms the enhancements brought by our algorithm with respect to visual association and readability.

Previous terrain rendering approaches have addressed the aspect of data compression and fast decoding for rendering, but applications where the terrain is repeatedly modified and needs to be buffered on disk have not been considered so far. Such applications require both decoding and encoding to be faster than disk transfer. We present a novel approach for editing gigasample terrain fields at interactive rates and high quality. To achieve high decoding and encoding throughput, we employ a compression scheme for height and pixel maps based on a sparse wavelet representation. On recent GPUs it can encode and decode up to 270 and 730 MPix/s of color data, respectively, at compression rates and quality superior to JPEG, and it achieves more than twice these rates for lossless height field compression. The construction and rendering of a height field triangulation is avoided by using GPU ray‐casting directly on the regular grid underlying the compression scheme. We show the efficiency of our method for interactive editing and continuous level‐of‐detail rendering of terrain fields comprised of several hundreds of gigasamples.

The efficient and high quality rendering of complex road networks—given as vector data—and high-resolution digital elevation models (DEMs) poses a significant problem in 3D geographic information systems. As in paper maps, a cartographic representation of roads with rounded caps and accentuated clearly distinguishable colors is desirable. On the other hand, advances in the technology of remote sensing have led to an explosion of the size and resolution of DEMs, making the integration of cartographic roads very challenging. In this work we investigate techniques for integrating such roads into a terrain renderer capable of handling high-resolution data sets. We evaluate the suitability of existing methods for draping vector data onto DEMs, and we adapt two methods for the rendering of cartographic roads by adding analytically computed rounded caps at the ends of road segments. We compare both approaches with respect to performance and quality, and we outline application areas in which either approach is preferable.

There seems to be little question that we live in an age of complexity, perhaps undue complexity. The interrelation of almost all the people all the time – at least those with internet access – the pressures of urbanisation, natural resource depletion, increasing traffic and pollution, and our ability to be anywhere at any time electronically have led to a rather complex living mode. Architects, heeding the call, have produced an architecture of corresponding complexity. Falling to architectural fashion trends, market pressures, and the surge of corporate capitalism, architects now give their clients just what they want, or what they themselves want to give them. There was a time when architectural form expressed some relation to reason, but that dated notion seems to be in little evidence today.