GPU-Based Large-Scale Visualization

    Tuesday, 19 November

    14:15 - 18:00

    Room S222

    Recent advances in image and volume acquisition as well as computational advances in simulation have led to an explosion of the amount of data that must be visualized and analyzed. Modern techniques combine the parallel processing power of GPUs with out-of-core methods and data streaming to enable the interactive visualization of giga- and terabytes of image and volume data. A major enabler for interactivity is making both the computational and the visualization effort proportional to the amount of data that is actually visible on screen, decoupling it from the full data size. This leads to powerful display-aware multi-resolution techniques that enable the visualization of data of almost arbitrary size.

    The course consists of two major parts: An introductory part that progresses from fundamentals to modern techniques, and a more advanced part that discusses details of ray-guided volume rendering, novel data structures for display-aware visualization and processing, and the remote visualization of large online data collections.

    You will learn how to develop efficient GPU data structures and large-scale visualizations, implement out-of-core strategies and concepts such as virtual texturing that have only been employed recently, as well as how to use modern multi-resolution representations. These approaches reduce the GPU memory requirements of extremely large data to a working set size that fits into current GPUs. You will learn how to perform ray-casting of volume data of almost arbitrary size and how to render and process gigapixel images using scalable, display-aware techniques. We will describe custom virtual texturing architectures as well as recent hardware developments in this area. We will also describe client/server systems for distributed visualization, on-demand data processing and streaming, and remote visualization.

    We will describe implementations using OpenGL as well as CUDA, exploiting parallelism on GPUs combined with additional asynchronous processing and data streaming on CPUs.



    Intended Audience

    We target researchers and practitioners in visualization and computer graphics that want to learn recent GPU techniques and hardware/API capabilities for implementing visualization systems that scale to very large data. The course will also be interesting for people interested in processing and rendering gigapixel images in a scalable manner.


    Course participants should have a basic understanding of how GPUs work and how they are programmed using standard graphics APIs such as OpenGL or Direct3D, and a basic understanding of general-purpose GPU programming using CUDA or OpenCL.


    Markus Hadwiger, King Abdullah University of Science and Technology
    Jens Krueger, University of Duisburg-Essen
    Johanna Beyer, Harvard University
    Stefan Bruckner, University of Bergen

    Markus Hadwiger is an Assistant Professor in computer science at King Abdullah University of Science and Technology and head of the High-Performance Visualization group at the Geometric Modeling and Scientific Visualization Center. Before joining KAUST, he was a Senior Researcher at VRVis Research Center in Vienna. He received a PhD in computer science from the Vienna University of Technology in 2004. He is a co-author of the book Real-Time Volume Graphics. His research interests include petascale visual computing and scientific visualization, volume rendering, large-scale image processing, and GPU algorithms and architecture.

    Jens Krueger finished his PhD in 2006 at the Technische Universitaet Muenchen. After Post Doc positions in Munich and at Scientific Computing and Imaging (SCI) Institute he became Research Assistant Professor at the University of Utah. In 2009 he joined the Cluster of Excellence to head the Interactive Visualization and Data Analysis group. Since 2013 he is Chair of the High Performance Computing group at the University of Duisburg-Essen. He also holds an adjunct professor title of the University of Utah and is a principal investigator in the Intel Visual Computing Institute.

    Johanna Beyer is a postdoctoral fellow in the School of Engineering and Applied Sciences at Harvard University. Before joining Harvard, she was a postdoctoral fellow at the Geometric Modeling and Scientific Visualization Center at King Abdullah University of Science and Technology. Her research interests include large-data visualization, parallel visualization, and GPU-based volume rendering for neuroscience and neurobiology. She received a PhD in computer science from the Vienna University of Technology in 2010.

    Stefan Bruckner received his Ph.D. in 2008 from Vienna University of Technology. He was awarded the habilitation (venia docendi) in Practical Computer Science in 2012. From 2008 to 2013, he was an assistant professor at the Institute of Computer Graphics and Algorithms at VUT. In 2009/2010, he spent one year as a visiting Postdoctoral Research Fellow at Simon Fraser University, Canada. Since March 2013, he is a full professor in visualization at the University of Bergen. His research interests include biomedical and illustrative visualization, volume rendering, and visual data exploration. In 2011, he was recipient of the Eurographics Young Researcher Award.