Interactive volume rendering of large datasets using the silicon graphics Onyx4 visualization system

technical reportMany recent approaches to interactive volume rendering have focused on leveraging the power of commodity graphics hardware. Though currently limited to relatively small datasets, these approaches have been overwhelmingly successful. As the size of volumetric datasets continues to grow at a rapid pace, the need for scalable systems capable of interactively visualizing large datasets has emerged. In an attempt to address this need, SGI, Inc. has introduced the Silicon Graphics Onyx4 family of visualization systems. We present the results of our preliminary investigation into the utility of an 8-pipe Onyx4 system for interactive volume rendering of large datasets. By rendering the image in parallel using an application called Rhesus, we find that the Onyx4 provides reasonable interactivity for datasets that consume as much as 512 MB of texture memory

Cluster-based interactive volume rendering with Simian

technical reportCommodity-based computer clusters offer a cost-effective alternative to traditional largescale, tightly coupled computers as a means to provide high-performance computational and visualization services. The Center for the Simulation of Accidental Fires and Explosions (C-SAFE) at the University of Utah employs such a cluster, and we have begun to experiment with cluster-based visualization services. In particular, we seek to develop an interactive volume rendering tool for navigating and visualizing large-scale scientific datasets. Using Simian, an OpenGL volume renderer, we examine two approaches to cluster-based interactive volume rendering: (1) a ?cluster-aware? version of the application that makes explicit use of remote nodes through a message-passing interface, and (2) the unmodified application running atop the Chromium clustered rendering framework. This paper provides a detailed comparison of the two approaches by carefully considering the key issues that arise when parallelizing Simian. These issues include the richness of user interaction; the distribution of volumetric datasets and proxy geometry; and the degree of interactivity provided by the image rendering and compositing schemes. The results of each approach when visualizing two large-scale C-SAFE datasets are given, and we discuss the relative advantages and disadvantages that were considered when developing our cluster-based interactive volume rendering application

Practical global illumination for interactive particle visualization

ManuscriptParticle-based simulation methods are used to model a wide range of complex phenomena and to solve time-dependent problems of various scales. Effective visualizations of the resulting state will communicate subtle changes in the three-dimensional structure, spatial organization, and qualitative trends within a simulation as it evolves. We present two algorithms targeting upcoming, highly parallel multicore desktop systems to enable interactive navigation and exploration of large particle datasets with global illumination effects. Monte Carlo path tracing and texture mapping are used to capture computationally expensive illumination effects such as soft shadows and diffuse interreflection. The first approach is based on precomputation of luminance textures and removes expensive illumination calculations from the interactive rendering pipeline. The second approach is based on dynamic luminance texture generation and decouples interactive rendering from the computation of global illumination effects. These algorithms provide visual cues that enhance the ability to perform analysis and feature detection tasks while interrogating the data at interactive rates. We explore the performance of these algorithms and demonstrate their effectiveness using several large datasets

Survey of the Itanium architecture from a programmer's perspective

Journal ArticleThe Itanium family of processors represents Intel;s foray into the world of Explicitly Parallel Instruction Computing and 64-bit system design. This survey contains an introduction to the Itanium architecture and instruction set, as well as some of the available implementations. Taking a programmer's perspective, we have attempted to distill the relevant information from a variety of sources, including the Intel Itanium architecture documentation

Enhancing Interactive Particle Visualization with Advanced Shading Models

Figure 1: Comparing shading models. When shading a surface, purely local models do not consider contributions from other surfaces in the environment (left). These models are often augmented with shadows to enhance cues about shape and relative position (middleleft). However, the constant ambient term used to approximate indirect illumination often obscures details in shadowed regions, providing ambiguous or conflicting information. More accurate shading models, for example, ambient occlusion (middle-right) and physically based diffuse interreflection (right), provide better perceptual cues in these regions. Particle-based simulation methods are used to model a wide range of complex phenomena and to solve time-dependent problems of various scales. Effective visualization of the resulting state should communicate subtle changes in the three-dimensional structure, spatial organization, and qualitative trends within a simulation as it evolves. We take steps toward understanding and using advanced shading models in the context of interactive particle visualization. Specifically, the impact of ambient occlusion and physically based diffuse interreflection is investigated using a formal user study. We find that these shading models provide additional visual cues that enable viewers to better understand subtle features within particle datasets. We also describe a visualization process that enables interactive navigation and exploration of large particle datasets, rendered with illumination effects from advanced shading models. Informal feedback from application scientists indicates that the results of this process enhance the data analysis tasks necessary for understanding complex particle datasets

Stream Filtering in StreamRay: An Architecture for Coherent Ray Tracing Stream Filtering in StreamRay: An Architecture for Coherent Ray Tracing Fig. 1. rtrt: A scene typical of interactive ray tracing scenarios, rendered with our simula- tor

Abstract The wide availability of commodity graphics processors has made real-time graphics an intrinsic component of the human/computer interface. These graphics cores accelerate the z-buffer algorithm and provide a highly interactive experience at a relatively low cost. However, many applications in entertainment, science, medicine, etc. require higher quality lighting effects such as accurate shadows and reflections. These effects are difficult to achieve with z-buffer algorithms but are much easier to achieve using ray tracing. Ray tracing is computationally more complex, but has better scaling and parallelism properties than the z-buffer approach. However, ray tracing patterns are difficult to predict and therefore, the parallelism promise is hard to achieve. This paper highlights a novel ray tracing approach based on stream filtering, and presents StreamRay, a multi-core wide SIMD machine that employs efficient address generation mechanisms to form a stream of rays for highly parallel SIMD execution. Results demonstrate that StreamRay delivers interactive frame rates of 15-25 fps for scenes of high geometric complexity while sustaining high utilization for SIMD widths of up to 16 elements. Abstract The wide availability of commodity graphics processors has made real-time graphics an intrinsic component of the human/computer interface. These graphics cores accelerate the zbuffer algorithm and provide a highly interactive experience at a relatively low cost. However, many applications in entertainment, science, medicine, etc. require higher quality lighting effects such as accurate shadows and reflections. These effects are difficult to achieve with z-buffer algorithms but are much easier to achieve using ray tracing. Ray tracing is computationally more complex, but has better scaling and parallelism properties than the zbuffer approach. However, ray tracing patterns are difficult to predict and therefore, the parallelism promise is hard to achieve. This paper highlights a novel ray tracing approach based on stream filtering, and presents StreamRay, a multi-core wide SIMD machine that employs efficient address generation mechanisms to form a stream of rays for highly parallel SIMD execution. Results demonstrate that StreamRay delivers interactive frame rates of 15-25 fps for scenes of high geometric complexity while sustaining high utilization for SIMD widths of up to 16 elements

Cluster-Based Interactive Volume Rendering with Simian

Commodity-based computer clusters offer a cost-effective alternative to traditional large-scale, tightly coupled computers as a means to provide high-performance computational and visualization services. The Center for the Simulation of Accidental Fires and Explosions (C-SAFE) at the University of Utah employs such a cluster, and we have begun to experiment with cluster-based visualization services. In particular, we seek to develop an interactive volume rendering tool for navigating and visualizing large-scale scientific datasets. Using Simian, an OpenGL volume renderer, we examine two approaches to cluster-based interactive volume rendering: (1) a “cluster-aware ” version of the application that makes explicit use of remote nodes through a message-passing interface, and (2) the unmodified application running atop the Chromium clustered rendering framework. This paper provides a detailed comparison of the two approaches by carefully considering the key issues that arise when parallelizing Simian. These issues include the richness of user interaction; the distribution of volumetric datasets and proxy geometry; and the degree of interactivity provided by the image rendering and compositing schemes. The results of each approach when visualizing two large-scale C-SAFE datasets are given, and we discuss the relative advantages and disadvantages that were considered when developing our cluster-based interactive volume rendering application