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Visualization is vital to the scientific discovery process.
An interactive high-fidelity rendering provides accelerated insight into complex structures, models and relationships.
However, the efficient mapping of visualization tasks to high performance architectures is often difficult, being subject to a challenging mixture of hardware and software architectural complexities in combination with domain-specific hurdles.
These difficulties are often exacerbated on heterogeneous architectures.
In this thesis, a variety of ray casting-based techniques are developed and investigated with respect to a more efficient usage of heterogeneous HPC systems for distributed visualization, addressing challenges in mesh-free rendering, in-situ compression, task-based workload formulation, and remote visualization at large scale.
A novel direct raytracing scheme for on-the-fly free surface reconstruction of particle-based simulations using an extended anisoptropic kernel model is investigated on different state-of-the-art cluster setups.
The versatile system renders up to 170 million particles on 32 distributed compute nodes at close to interactive frame rates at 4K resolution with ambient occlusion.
To address the widening gap between high computational throughput and prohibitively slow I/O subsystems, in situ topological contour tree analysis is combined with a compact image-based data representation to provide an effective and easy-to-control trade-off between storage overhead and visualization fidelity.
Experiments show significant reductions in storage requirements, while preserving flexibility for exploration and analysis.
Driven by an increasingly heterogeneous system landscape, a flexible distributed direct volume rendering and hybrid compositing framework is presented.
Based on a task-based dynamic runtime environment, it enables adaptable performance-oriented deployment on various platform configurations.
Comprehensive benchmarks with respect to task granularity and scaling are conducted to verify the characteristics and potential of the novel task-based system design.
A core challenge of HPC visualization is the physical separation of visualization resources and end-users.
Using more tiles than previously thought reasonable, a distributed, low-latency multi-tile streaming system is demonstrated, being able to sustain a stable 80 Hz when streaming up to 256 synchronized 3840x2160 tiles and achieve 365 Hz at 3840x2160 for sort-first compositing over the internet, thereby enabling lightweight visualization clients and leaving all the heavy lifting to the remote supercomputer.
Interactive visualization of large structured and unstructured data sets is a permanent challenge for scientific visualization. Large data sets are for example created by magnetic resonance imaging (MRI), computed tomography (CT), Computational fluid dynamics (CFD) finite element method (FEM), and computer aided design (CAD). For visualizing those data sets not only accelerated rasterization by means of using specialized hardware i.e. graphics cards is of interest, but also ray casting, as it is perfectly suited for scientific visualization. Ray casting does not only support many rendering modes (e.g., opaque rendering, semi transparent rendering, iso surface rendering, maximum intensity projection, x-ray, absorption emitter model, ...) for which it allows the creation of high quality images, but it also supports many primitives (e.g., not only triangles but also spheres, curved iso surfaces, NURBS, implicit functions, ...). It furthermore scales basically linear to the amount of processor cores used and - this makes it highly interesting for the visualization of large data sets - it scales for static scenes sublinear to data size. Interactive ray casting is currently not widely used within the scientifc visualization community. This is mainly based on historical reasons, as just a few years ago no applicable interactive ray casters for commodity hardware did exist. Interactive scientific visualization has only been possible by using graphics cards or specialized and/or expensive hardware. The goal of this work is to broaden the possibilies for interactive scientific visualization, by showing that interactive CPU based ray casting is today feasible on commodity hardware and that it may efficiently be used together with GPU based rasterization. In this thesis it is first shown that interactive CPU based ray casters may efficiently be integrated into already existing OpenGL frameworks. This is achieved through an OpenGL friendly interface that supports multiple threads and single instruction multiple data (SIMD) operations. For the visualization of rectilinear (and not necessarily cartesian) grids are new implicit kd-trees introduced. They have fast construction times, low memory requirements, and allow ontoday's commodity desktop machines interactive iso surface ray tracing and maximum intensity projection of large scalar fields. A new interactive SIMD ray tracing technique for large tetrahedral meshes is introduced. It is very portable and general and is therefore suited for portation upon different (future) hardware and for usage upon several applications. The thesis ends with a real life commercial application which shows that CPU-based ray casting has already reached the state where it may outperform GPU-based rasterization for scientific visualization.
Feature Based Visualization
(2007)
In this thesis we apply powerful mathematical tools such as interval arithmetic for applications in computational geometry, visualization and computer graphics, leading to robust, general and efficient algorithms. We present a completely novel approach for computing the arrangement of arbitrary implicit planar curves and perform ray casting of arbitrary implicit functions by jointly achieving, for the first time, robustness, efficiency and flexibility. Indeed we are able to render even the most difficult implicits in real-time with guaranteed topology and at high resolution. We use subdivision and interval arithmetic as key-ingredients to guarantee robustness. The presented framework is also well-suited for applications to large and unstructured data sets due to the inherent adaptivity of the techniques that are used. We also approach the topic of tensors by collaborating with mechanical engineers on comparative tensor visualization and provide them with helpful visualization paradigms to interpret the data.