Granite: A scientific database model and implementation

The principal goal of this research was to develop a formal comprehensive model for representing highly complex scientific data. An effective model should provide a conceptually uniform way to represent data and it should serve as a framework for the implementation of an efficient and easy-to-use software environment that implements the model. The dissertation work presented here describes such a model and its contributions to the field of scientific databases. In particular, the Granite model encompasses a wide variety of datatypes used across many disciplines of science and engineering today. It is unique in that it defines dataset geometry and topology as separate conceptual components of a scientific dataset. We provide a novel classification of geometries and topologies that has important practical implications for a scientific database implementation. The Granite model also offers integrated support for multiresolution and adaptive resolution data. Many of these ideas have been addressed by others, but no one has tried to bring them all together in a single comprehensive model. The datasource portion of the Granite model offers several further contributions. In addition to providing a convenient conceptual view of rectilinear data, it also supports multisource data. Data can be taken from various sources and combined into a unified view. The rod storage model is an abstraction for file storage that has proven an effective platform upon which to develop efficient access to storage. Our spatial prefetching technique is built upon the rod storage model, and demonstrates very significant improvement in access to scientific datasets, and also allows machines to access data that is far too large to fit in main memory. These improvements bring the extremely large datasets now being generated in many scientific fields into the realm of tractability for the ordinary researcher. We validated the feasibility and viability of the model by implementing a significant portion of it in the Granite system. Extensive performance evaluations of the implementation indicate that the features of the model can be provided in a user-friendly manner with an efficiency that is competitive with more ad hoc systems and more specialized application specific solutions

Scalable Real-Time Rendering for Extremely Complex 3D Environments Using Multiple GPUs

In 3D visualization, real-time rendering of high-quality meshes in complex 3D environments is still one of the major challenges in computer graphics. New data acquisition techniques like 3D modeling and scanning have drastically increased the requirement for more complex models and the demand for higher display resolutions in recent years. Most of the existing acceleration techniques using a single GPU for rendering suffer from the limited GPU memory budget, the time-consuming sequential executions, and the finite display resolution. Recently, people have started building commodity workstations with multiple GPUs and multiple displays. As a result, more GPU memory is available across a distributed cluster of GPUs, more computational power is provided throughout the combination of multiple GPUs, and a higher display resolution can be achieved by connecting each GPU to a display monitor (resulting in a tiled large display configuration). However, using a multi-GPU workstation may not always give the desired rendering performance due to the imbalanced rendering workloads among GPUs and overheads caused by inter-GPU communication. In this dissertation, I contribute a multi-GPU multi-display parallel rendering approach for complex 3D environments. The approach has the capability to support a high-performance and high-quality rendering of static and dynamic 3D environments. A novel parallel load balancing algorithm is developed based on a screen partitioning strategy to dynamically balance the number of vertices and triangles rendered by each GPU. The overhead of inter-GPU communication is minimized by transferring only a small amount of image pixels rather than chunks of 3D primitives with a novel frame exchanging algorithm. The state-of-the-art parallel mesh simplification and GPU out-of-core techniques are integrated into the multi-GPU multi-display system to accelerate the rendering process

Efficient rendering of large 3-D and 4-D scalar fields

Rendering volumetric data, as a compute/communication intensive and highly parallel application, represents the characteristics of future workloads for desktop computers. Interactively rendering volumetric data has been a challenging problem due to its high computational and communication requirements. With the consistent trend toward high resolution data, it has remained a difficult problem despite the continuous increase in processing power, because of the increasing performance gap between computation and communication. On the other hand, the new multi-core architecture trend in computational units in PC, which can be characterized by parallelism and heterogeneity, provides both opportunities and challenges. While the new on-chip parallel architectures offer opportunities for extremely high performance, widespread use of those parallel processors requires extensive changes in previous algorithms to take advantage of the new architectures. In this dissertation, we develop new methods and techniques to support interactive rendering of large volumetric data. In particular, we present a novel method to layout data on disk for efficiently performing an out-of-core axis-aligned slicing of large multidimensional scalar fields. We also present a new method to efficiently build an out-of-core indexing structure for n-dimensional volumetric data. Then, we describe a streaming model for efficiently implementing volume ray casting on a heterogeneous compute resource environment. We describe how we implement the model on SONY/TOSHIBA/IBM Cell Broadband Engine and on NVIDIA CUDA architecture. Our results show that our out-of-core techniques significantly reduce the communication bandwidth requirements and that our streaming model very effectively makes use of the strengths of those heterogeneous parallel compute resource environment for volume rendering. In all cases, we achieve scalability and load balancing, while hiding memory latency

Diamond-based models for scientific visualization

Hierarchical spatial decompositions are a basic modeling tool in a variety of application domains including scientific visualization, finite element analysis and shape modeling and analysis. A popular class of such approaches is based on the regular simplex bisection operator, which bisects simplices (e.g. line segments, triangles, tetrahedra) along the midpoint of a predetermined edge. Regular simplex bisection produces adaptive simplicial meshes of high geometric quality, while simplifying the extraction of crack-free, or conforming, approximations to the original dataset. Efficient multiresolution representations for such models have been achieved in 2D and 3D by clustering sets of simplices sharing the same bisection edge into structures called diamonds. In this thesis, we introduce several diamond-based approaches for scientific visualization. We first formalize the notion of diamonds in arbitrary dimensions in terms of two related simplicial decompositions of hypercubes. This enables us to enumerate the vertices, simplices, parents and children of a diamond. In particular, we identify the number of simplices involved in conforming updates to be factorial in the dimension and group these into a linear number of subclusters of simplices that are generated simultaneously. The latter form the basis for a compact pointerless representation for conforming meshes generated by regular simplex bisection and for efficiently navigating the topological connectivity of these meshes. Secondly, we introduce the supercube as a high-level primitive on such nested meshes based on the atomic units within the underlying triangulation grid. We propose the use of supercubes to associate information with coherent subsets of the full hierarchy and demonstrate the effectiveness of such a representation for modeling multiresolution terrain and volumetric datasets. Next, we introduce Isodiamond Hierarchies, a general framework for spatial access structures on a hierarchy of diamonds that exploits the implicit hierarchical and geometric relationships of the diamond model. We use an isodiamond hierarchy to encode irregular updates to a multiresolution isosurface or interval volume in terms of regular updates to diamonds. Finally, we consider nested hypercubic meshes, such as quadtrees, octrees and their higher dimensional analogues, through the lens of diamond hierarchies. This allows us to determine the relationships involved in generating balanced hypercubic meshes and to propose a compact pointerless representation of such meshes. We also provide a local diamond-based triangulation algorithm to generate high-quality conforming simplicial meshes

Multiple dataset visualization (MDV) framework for scalar volume data

Many applications require comparative analysis of multiple datasets representing different samples, conditions, time instants, or views in order to develop a better understanding of the scientific problem/system under consideration. One effective approach for such analysis is visualization of the data. In this PhD thesis, we propose an innovative multiple dataset visualization (MDV) approach in which two or more datasets of a given type are rendered concurrently in the same visualization. MDV is an important concept for the cases where it is not possible to make an inference based on one dataset, and comparisons between many datasets are required to reveal cross-correlations among them. The proposed MDV framework, which deals with some fundamental issues that arise when several datasets are visualized together, follows a multithreaded architecture consisting of three core components, data preparation/loading, visualization and rendering. The visualization module - the major focus of this study, currently deals with isosurface extraction and texture-based rendering techniques. For isosurface extraction, our all-in-memory approach keeps datasets under consideration and the corresponding geometric data in the memory. Alternatively, the only-polygons- or points-in-memory only keeps the geometric data in memory. To address the issues related to storage and computation, we develop adaptive data coherency and multiresolution schemes. The inter-dataset coherency scheme exploits the similarities among datasets to approximate the portions of isosurfaces of datasets using the isosurface of one or more reference datasets whereas the intra/inter-dataset multiresolution scheme processes the selected portions of each data volume at varying levels of resolution. The graphics hardware-accelerated approaches adopted for MDV include volume clipping, isosurface extraction and volume rendering, which use 3D textures and advanced per fragment operations. With appropriate user-defined threshold criteria, we find that various MDV techniques maintain a linear time-N relationship, improve the geometry generation and rendering time, and increase the maximum N that can be handled (N: number of datasets). Finally, we justify the effectiveness and usefulness of the proposed MDV by visualizing 3D scalar data (representing electron density distributions in magnesium oxide and magnesium silicate) from parallel quantum mechanical simulation

Scalable exploration of highly detailed and annotated 3D models

With the widespread availability of mobile graphics terminals andWebGL-enabled browsers, 3D graphics over the Internet is thriving. Thanks to recent advances in 3D acquisition and modeling systems, high-quality 3D models are becoming increasingly common, and are now potentially available for ubiquitous exploration. In current 3D repositories, such as Blend Swap, 3D Café or Archive3D, 3D models available for download are mostly presented through a few user-selected static images. Online exploration is limited to simple orbiting and/or low-fidelity explorations of simplified models, since photorealistic rendering quality of complex synthetic environments is still hardly achievable within the real-time constraints of interactive applications, especially on on low-powered mobile devices or script-based Internet browsers. Moreover, navigating inside 3D environments, especially on the now pervasive touch devices, is a non-trivial task, and usability is consistently improved by employing assisted navigation controls. In addition, 3D annotations are often used in order to integrate and enhance the visual information by providing spatially coherent contextual information, typically at the expense of introducing visual cluttering. In this thesis, we focus on efficient representations for interactive exploration and understanding of highly detailed 3D meshes on common 3D platforms. For this purpose, we present several approaches exploiting constraints on the data representation for improving the streaming and rendering performance, and camera movement constraints in order to provide scalable navigation methods for interactive exploration of complex 3D environments. Furthermore, we study visualization and interaction techniques to improve the exploration and understanding of complex 3D models by exploiting guided motion control techniques to aid the user in discovering contextual information while avoiding cluttering the visualization. We demonstrate the effectiveness and scalability of our approaches both in large screen museum installations and in mobile devices, by performing interactive exploration of models ranging from 9Mtriangles to 940Mtriangles

Massive model visualization: An investigation into spatial partitioning

The current generation of visualization software is incapable of handling the interactive rendering of arbitrarily large models. While many solutions have been proposed for Massive Model Visualization, very few are able to achieve the full capabilities needed for a computer visualization solution. In most cases this is due to overly complex approaches that, while achieving impressive frame rates, make it virtually impossible to implement features like part manipulation. What is needed is a simple approach with rendering performance bounded by screen complexity not model size, with primitive traceability to the original model to facilitate part manipulation, and capability to be modified in near-real-time. This thesis introduces MMDr, a simple system to achieve interactive frame rates on extremely large data sets, while retaining support for most if not all the features required for a computer visualization solution