Parallel Texture-Based Vector Field Visualization on Curved Surfaces Using GPU Cluster Computers

We adopt a technique for texture-based visualization of flow fields on curved surfaces for parallel computation on a GPU cluster. The underlying LIC method relies on image-space calculations and allows the user to visualize a full 3D vector field on arbitrary and changing hypersurfaces. By using parallelization, both the visualization speed and the maximum data set size are scaled with the number of cluster nodes. A sort-first strategy with image-space decomposition is employed to distribute the workload for the LIC computation, while a sort-last approach with an object-space partitioning of the vector field is used to increase the total amount of available GPU memory. We specifically address issues for parallel GPU-based vector field visualization, such as reduced locality of memory accesses caused by particle tracing, dynamic load balancing for changing camera parameters, and the combination of image-space and object-space decomposition in a hybrid approach. Performance measurements document the behavior of our implementation on a GPU cluster with AMD Opteron CPUs, NVIDIA GeForce 6800 Ultra GPUs, and Infiniband network connection

Action-Based Multifield Video Visualization

One challenge in video processing is to detect actions and events, known or unknown, in video streams dynamically. This paper proposes a visualization solution, where a video stream is depicted as a series of snapshots at a relatively sparse interval, and detected actions are highlighted with continuous abstract illustrations. The combined imagery and illustrative visualization conveys multi-field information in a manner similar to electrocardiograms (ECG) and seismographs. We thus name this type of video visualization as VideoPerpetuoGram (VPG). In this paper, we describe a system that handles the aw and processed information of the video stream in a multi-field visualization pipeline. As examples, we consider the needs for highlighting several types of processed information, including detected actions in video streams, and estimated relationship between recognized objects. We examine the effective means for depicting multi-field information in VPG, and support our choice of visual mappings through a survey. Our GPU implementation facilitates the VPG-specific viewing specification through a sheared object space, as well as volume bricking and combinational rendering of volume data and glyphs

Parallel Texture-Based Vector Field Visualization on Curved Surfaces Using GPU Cluster Computers

We adopt a technique for texture-based visualization of flow fields on curved surfaces for parallel computation on a GPU cluster. The underlying LIC method relies on image-space calculations and allows the user to visualize a full 3D vector field on arbitrary and changing hypersurfaces. By using parallelization, both the visualization speed and the maximum data set size are scaled with the number of cluster nodes. A sort-first strategy with image-space decomposition is employed to distribute the workload for the LIC computation, while a sort-last approach with an object-space partitioning of the vector field is used to increase the total amount of available GPU memory. We specifically address issues for parallel GPU-based vector field visualization, such as reduced locality of memory accesses caused by particle tracing, dynamic load balancing for changing camera parameters, and the combination of image-space and object-space decomposition in a hybrid approach. Performance measurements document the behavior of our implementation on a GPU cluster with AMD Opteron CPUs, NVIDIA GeForce 6800 Ultra GPUs, and Infiniband network connection. Categories and Subject Descriptors (according to ACM CCS): I.3.3 [Computer Graphics]: Viewing algorithms I.3.3 [Three-Dimensional Graphics and Realism]: Color, shading, shadowing, and textur