Distributed Volume Rendering on a Visualization Cluster

Frank, S.; Kaufman, Arie

doi:10.1109/cad-cg.2005.34

Cited by 10 publications

(4 citation statements)

References 15 publications

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“…Out‐of‐core methods include algorithms that focus on accessing [PF02] and prefetching [CKS03] data, creating on‐the‐fly ghost data for bricked representations [ILC10], and methods for computing multi‐resolution hierarchies [HBJP12] or other processing tasks such as segmentation [FK05], PDE solvers [SSJ*11], image registration and alignment [JST*10], or level set computation [LKHW04]. Silva et al .…”

Section: Scalable Processing Strategiesmentioning

confidence: 99%

State‐of‐the‐Art in GPU‐Based Large‐Scale Volume Visualization

Beyer

Hadwiger

Pfister

2015

Computer Graphics Forum

104

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This survey gives an overview of the current state of the art in GPU techniques for interactive large‐scale volume visualization. Modern techniques in this field have brought about a sea change in how interactive visualization and analysis of giga‐, tera‐ and petabytes of volume data can be enabled on GPUs. In addition to combining the parallel processing power of GPUs with out‐of‐core methods and data streaming, a major enabler for interactivity is making both the computational and the visualization effort proportional to the amount and resolution of data that is actually visible on screen, i.e. ‘output‐sensitive’ algorithms and system designs. This leads to recent output‐sensitive approaches that are ‘ray‐guided’, ‘visualization‐driven’ or ‘display‐aware’. In this survey, we focus on these characteristics and propose a new categorization of GPU‐based large‐scale volume visualization techniques based on the notions of actual output‐resolution visibility and the current working set of volume bricks—the current subset of data that is minimally required to produce an output image of the desired display resolution. Furthermore, we discuss the differences and similarities of different rendering and data traversal strategies in volume rendering by putting them into a common context—the notion of address translation. For our purposes here, we view parallel (distributed) visualization using clusters as an orthogonal set of techniques that we do not discuss in detail but that can be used in conjunction with what we present in this survey.

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Section: Scalable Processing Strategiesmentioning

confidence: 99%

State‐of‐the‐Art in GPU‐Based Large‐Scale Volume Visualization

Beyer

Hadwiger

Pfister

2015

Computer Graphics Forum

104

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“…If the z ‐length of the slab is larger than a given slab minimum and either a split or merge is detected, or if the z‐length is equal to a given slab maximum, then a z‐cut is made. The concepts of a split and a merge are introduced in [FK05]. A split occurs when a slice contains two distinct non‐empty regions that both overlap a single region in the current slab.…”

Section: Out‐of‐core Data Managementmentioning

confidence: 99%

Out‐of‐Core and Dynamic Programming for Data Distribution on a Volume Visualization Cluster

Frank

Kaufman

2009

Computer Graphics Forum

Self Cite

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Ray directed volume-rendering algorithms are well suited for parallel implementation in a distributed cluster environment. For distributed ray casting, the scene must be partitioned between nodes for good load balancing, and a strict view-dependent priority order is required for image composition. In this paper, we define the load balanced network distribution (LBND) problem and map it to the NP-complete precedence constrained job-shop scheduling problem. We introduce a kd-tree solution and a dynamic programming solution. To process a massive data set, either a parallel or an out-of-core approach is required. Parallel preprocessing is performed by render nodes on data, which are allocated using a static data structure. Volumetric data sets often contain a large portion of voxels that will never be rendered, or empty space. Parallel preprocessing fails to take advantage of this. Our slab-projection slice, introduced in this paper, tracks empty space across consecutive slices of data to reduce the amount of data distributed and rendered. It is used to facilitate out-of-core bricking and kd-tree partitioning. Load balancing using each of our approaches is compared with traditional methods using several segmented regions of the Visible Korean data set.

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“…Much effort has been devoted to developing efficient compositing methods including Hsu's direct send method (Hsu, 1993), Camahort and Chakravarty's projection method (Camahort, 1993), the binary-swap method (Ma, 1994) and the divided-screenwise hierarchical compositing method (Ino, 2003). Object-order implementations have been developed for distributed-memory clusters (Elvins, 1992;Gribble, 2004;Müller, 2006), and specialized hardware has been developed that implements the binary-swap compositing method (Lombeyda, 2001, Muraki, 2003, Frank, 2005.…”

Section: Data Intensive Renderingmentioning

confidence: 99%

Visualization of Large-Scale Distributed Data

Leigh

Johnson

Renambot

et al.

Advances in Systems Analysis, Software Engineering, and High Performance Computing

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An effective visualization is best achieved through the creation of a proper representation of data and the interactive manipulation and querying of the visualization. Large-scale data visualization is particularly challenging because the size of the data is several orders of magnitude larger than what can be managed on an average desktop computer. Large-scale data visualization therefore requires the use of distributed computing. By leveraging the widespread expansion of the Internet and other national and international high-speed network infrastructure such as the National LambdaRail, Internet-2, and the Global Lambda Integrated Facility, data and service providers began to migrate toward a model of widespread distribution of resources. This chapter introduces different instantiations of the visualization pipeline and the historic motivation for their creation. The authors examine individual components of the pipeline in detail to understand the technical challenges that must be solved in order to ensure continued scalability. They discuss distributed data management issues that are specifically relevant to large-scale visualization. They also introduce key data rendering techniques and explain through case studies approaches for scaling them by leveraging distributed computing. Lastly they describe advanced display technologies that are now considered the “lenses” for examining large-scale data.

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Distributed Volume Rendering on a Visualization Cluster

Cited by 10 publications

References 15 publications

State‐of‐the‐Art in GPU‐Based Large‐Scale Volume Visualization

State‐of‐the‐Art in GPU‐Based Large‐Scale Volume Visualization

Out‐of‐Core and Dynamic Programming for Data Distribution on a Volume Visualization Cluster

Visualization of Large-Scale Distributed Data

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