Tensor approximation in visualization and graphics

In this course, we will introduce the basic concepts of tensor approximation (TA) – a higher-order generalization of the SVD and PCA methods – as well as its applications to visual data representation, analysis and visualization, and bring the TA framework closer to visualization and computer graphics researchers and practitioners. The course will cover the theoretical background of TA methods, their properties and how to compute them, as well as practical applications of TA methods in visualization and computer graphics contexts. In a first theoretical part, the attendees will be instructed on the necessary mathematical background of TA methods to learn the basics skills of using and applying these new tools in the context of the representation of large multidimensional visual data. Specific and very noteworthy features of the TA framework are highlighted which can effectively be exploited for spatio-temporal multidimensional data representation and visualization purposes. In two application oriented sessions, compact TA data representation in scientific visualization and computer graphics as well as decomposition and reconstruction algorithms will be demonstrated. At the end of the course, the participants will have a good basic knowledge of TA methods along with a practical understanding of its potential application in visualization and graphics related projects

Accelerating data-intensive scientific visualization and computing through parallelization

Many extreme-scale scientific applications generate colossal amounts of data that require an increasing number of processors for parallel processing. The research in this dissertation is focused on optimizing the performance of data-intensive parallel scientific visualization and computing. In parallel scientific visualization, there exist three well-known parallel architectures, i.e., sort-first/middle/last. The research in this dissertation studies the composition stage of the sort-last architecture for scientific visualization and proposes a generalized method, namely, Grouping More and Pairing Less (GMPL), for order-independent image composition workflow scheduling in sort-last parallel rendering. The technical merits of GMPL are two-fold: i) it takes a prime factorization-based approach for processor grouping, which not only obviates the common restriction in existing methods on the total number of processors to fully utilize computing resources, but also breaks down processors to the lowest level with a minimum number of peers in each group to achieve high concurrency and save communication cost; ii) within each group, it employs an improved direct send method to narrow down each processor’s pairing scope to further reduce communication overhead and increase composition efficiency. The performance superiority of GMPL over existing methods is evaluated through rigorous theoretical analysis and further verified by extensive experimental results on a high-performance visualization cluster. The research in this dissertation also parallelizes the over operator, which is commonly used for α-blending in various visualization techniques. Compared with its predecessor, the fully generalized over operator is n-operator compatible. To demonstrate the advantages of the proposed operator, the proposed operator is applied to the asynchronous and order-dependent image composition problem in parallel visualization. In addition, the dissertation research also proposes a very-high-speed pipeline-based architecture for parallel sort-last visualization of big data by developing and integrating three component techniques: i) a fully parallelized per-ray integration method that significantly reduces the number of iterations required for image rendering; ii) a real-time over operator that not only eliminates the restriction of pre-sorting and order-dependency, but also facilitates a high degree of parallelization for image composition. In parallel scientific computing, the research goal is to optimize QR decomposition, which is one primary algebraic decomposition procedure and plays an important role in scientific computing. QR decomposition produces orthogonal bases, i.e.,“core” bases for a given matrix, and oftentimes can be leveraged to build a complete solution to many fundamental scientific computing problems including Least Squares Problem, Linear Equations Problem, Eigenvalue Problem. A new matrix decomposition method is proposed to improve time efficiency of parallel computing and provide a rigorous proof of its numerical stability. The proposed solutions demonstrate significant performance improvement over existing methods for data-intensive parallel scientific visualization and computing. Considering the ever-increasing data volume in various science domains, the research in this dissertation have a great impact on the success of next-generation large-scale scientific applications

Rendering techniques for multimodal data

Many different direct volume rendering methods have been developed to visualize 3D scalar fields on uniform rectilinear grids. However, little work has been done on rendering simultaneously various properties of the same 3D region measured with different registration devices or at different instants of time. The demand for this type of visualization is rapidly increasing in scientific applications such as medicine in which the visual integration of multiple modalities allows a better comprehension of the anatomy and a perception of its relationships with activity. This paper presents different strategies of Direct Multimodal Volume Rendering (DMVR). It is restricted to voxel models with a known 3D rigid alignment transformation. The paper evaluates at which steps of the render-ing pipeline must the data fusion be realized in order to accomplish the desired visual integration and to provide fast re-renders when some fusion parameters are modified. In addition, it analyzes how existing monomodal visualization al-gorithms can be extended to multiple datasets and it compares their efficiency and their computational cost.Postprint (published version

Ten Simple Rules for Reproducible Research in Jupyter Notebooks

Reproducibility of computational studies is a hallmark of scientific methodology. It enables researchers to build with confidence on the methods and findings of others, reuse and extend computational pipelines, and thereby drive scientific progress. Since many experimental studies rely on computational analyses, biologists need guidance on how to set up and document reproducible data analyses or simulations. In this paper, we address several questions about reproducibility. For example, what are the technical and non-technical barriers to reproducible computational studies? What opportunities and challenges do computational notebooks offer to overcome some of these barriers? What tools are available and how can they be used effectively? We have developed a set of rules to serve as a guide to scientists with a specific focus on computational notebook systems, such as Jupyter Notebooks, which have become a tool of choice for many applications. Notebooks combine detailed workflows with narrative text and visualization of results. Combined with software repositories and open source licensing, notebooks are powerful tools for transparent, collaborative, reproducible, and reusable data analyses

Advanced Visualization and Intuitive User Interface Systems for Biomedical Applications

Modern scientific research produces data at rates that far outpace our ability to comprehend and analyze it. Such sources include medical imaging data and computer simulations, where technological advancements and spatiotemporal resolution generate increasing amounts of data from each scan or simulation. A bottleneck has developed whereby medical professionals and researchers are unable to fully use the advanced information available to them. By integrating computer science, computer graphics, artistic ability and medical expertise, scientific visualization of medical data has become a new field of study. The objective of this thesis is to develop two visualization systems that use advanced visualization, natural user interface technologies and the large amount of biomedical data available to produce results that are of clinical utility and overcome the data bottleneck that has developed. Computational Fluid Dynamics (CFD) is a tool used to study the quantities associated with the movement of blood by computer simulation. We developed methods of processing spatiotemporal CFD data and displaying it in stereoscopic 3D with the ability to spatially navigate through the data. We used this method with two sets of display hardware: a full-scale visualization environment and a small-scale desktop system. The advanced display and data navigation abilities provide the user with the means to better understand the relationship between the vessel\u27s form and function. Low-cost 3D, depth-sensing cameras capture and process user body motion to recognize motions and gestures. Such devices allow users to use hand motions as an intuitive interface to computer applications. We developed algorithms to process and prepare the biomedical and scientific data for use with a custom control application. The application interprets user gestures as commands to a visualization tool and allows the user to control the visualization of multi-dimensional data. The intuitive interface allows the user to control the visualization of data without manual contact with an interaction device. In developing these methods and software tools we have leveraged recent trends in advanced visualization and intuitive interfaces in order to efficiently visualize biomedical data in such a way that provides meaningful information that can be used to further appreciate it

An image-based approach to interactive crease extraction and rendering

AbstractRidge and valley manifolds are receiving a growing attention in visualization research due to their ability to reveal the shapes of salient structures in numerical datasets across scientific, engineering, and medical applications. However, the methods proposed to date for their extraction in the visualization and image analysis literature are computationally expensive and typically applied in an offline setting. This setup does not properly support a userdriven exploration, which often requires control over various parameters tuned to filter false positives and spurious artifacts and highlight the most significant structures. This paper presents a GPU-based adaptive technique for crease extraction and visualization across scales. Our method combines a scale-space analysis of the data in pre-processing with a ray casting approach supporting a robust and efficient one-dimensional numerical search, and an image-based rendering strategy. This general framework achieves high-quality crease surface representations at interactive frame rates. Results are proposed for analytical, medical, and computational datasets

Numerical Algorithm for the Topology of Singular Plane Curves

International audienceWe are interested in computing the topology of plane singular curves. For this, the singular points must be isolated. Numerical methods for isolating singular points are efficient but not certified in general. We are interested in developing certified numerical algorithms for isolating the singularities. In order to do so, we restrict our attention to the special case of plane curves that are projections of smooth curves in higher dimensions. In this setting, we show that the singularities can be encoded by a regular square system whose isolation can be certified by numerical methods. This type of curves appears naturally in robotics applications and scientific visualization

Methods and design issues for next generation network-aware applications

Networks are becoming an essential component of modern cyberinfrastructure and this work describes methods of designing distributed applications for high-speed networks to improve application scalability, performance and capabilities. As the amount of data generated by scientific applications continues to grow, to be able to handle and process it, applications should be designed to use parallel, distributed resources and high-speed networks. For scalable application design developers should move away from the current component-based approach and implement instead an integrated, non-layered architecture where applications can use specialized low-level interfaces. The main focus of this research is on interactive, collaborative visualization of large datasets. This work describes how a visualization application can be improved through using distributed resources and high-speed network links to interactively visualize tens of gigabytes of data and handle terabyte datasets while maintaining high quality. The application supports interactive frame rates, high resolution, collaborative visualization and sustains remote I/O bandwidths of several Gbps (up to 30 times faster than local I/O). Motivated by the distributed visualization application, this work also researches remote data access systems. Because wide-area networks may have a high latency, the remote I/O system uses an architecture that effectively hides latency. Five remote data access architectures are analyzed and the results show that an architecture that combines bulk and pipeline processing is the best solution for high-throughput remote data access. The resulting system, also supporting high-speed transport protocols and configurable remote operations, is up to 400 times faster than a comparable existing remote data access system. Transport protocols are compared to understand which protocol can best utilize high-speed network connections, concluding that a rate-based protocol is the best solution, being 8 times faster than standard TCP. An HD-based remote teaching application experiment is conducted, illustrating the potential of network-aware applications in a production environment. Future research areas are presented, with emphasis on network-aware optimization, execution and deployment scenarios