CGLX: A Scalable, High-Performance Visualization Framework for Networked Display Environments

Doerr, Kai; Kuester, Falko

doi:10.1109/tvcg.2010.59

Cited by 86 publications

(42 citation statements)

References 17 publications

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“…When a client's status is changed, consequently, the client node will send the changes to the server making the others clients fetch these new data modified by any client. OptiPlanet [31], Equalizer [7], Chromium [14] and CGLX [6] are examples of interactive applications to be executed in a cluster display wall environment. Non-interactive applications do not usually allow any changes by the user, although, in the case that the user could make some, these would not affect either the server or the other clients.…”

Section: Background Of Cluster Visualization Systemsmentioning

confidence: 99%

Analyzing Google Earth application in a heterogeneous commodity cluster display wall

Arroyo

Giné

Roig

et al. 2015

Multimed Tools Appl

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Nowadays, clusters of interconnected workstations have become a common solution for powering large composite displays, or "cluster display walls", to visualize high resolution images. Our paper is focused on analyzing a specific cluster display wall developed by Google, named Liquid Galaxy, made up of heterogeneous commodity hardware with different degrees of heterogeneity, running master-slave (Google Earth) and clientserver (Quake III Arena) multimedia applications. With this in mind, we define and test different scenarios, representing the behavior of many kinds of users. Our results show that the CPU, memory and network are good enough to execute the client-server application, while, depending on the user behavior, the external network constitutes the bottleneck of the system in Google Earth. So, the master-slave application has focused our attention. Likewise, in order to analyze the users' point of view when interacting with Google Earth in the Liquid Galaxy, we define a new metric, named Visualization Rate (VR), which enables a relationship to be established between the user experience and the platform performance. In order to set the minimum acceptable value of the VR parameter according to users perception, we carried out different tests with real users. Then, this minimum threshold was compared with the VR value obtained from the automated benchmarking performed afterwards on clusters with different heterogeneity degrees. Finally, we analyzed the VR trend when the Liquid Galaxy is scaled from 3 up to 8 nodes in both the homogeneous and heterogeneous architectures to study the scalability of the system.

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Section: Background Of Cluster Visualization Systemsmentioning

confidence: 99%

Analyzing Google Earth application in a heterogeneous commodity cluster display wall

Arroyo

Giné

Roig

et al. 2015

Multimed Tools Appl

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“…Specific examples include rendering every Nth frame or distributing primitives to different pipelines based on screen space. Some of these systems and APIs like Chromium [8], Garuda [9], NetJuggler [2], CGLX [3] and Equalizer [5] provide scalable rendering on shared memory systems.…”

Section: Prior Workmentioning

confidence: 99%

Distributed massive model rendering

Narayanan

2012

Proceedings of the Eighth Indian Conference on Computer Vision, Graphics and Image Processing

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Graphics models are getting increasingly bulkier with detailed geometry, textures, normal maps, etc. There is a lot of interest to model and navigate through detailed models of large monuments. Many monuments of interest have both rich detail and large spatial extent. Rendering them for navigation on a single workstation is practically impossible, even given the power of today's CPUs and GPUs. Many models may not fit the GPU memory, the CPU memory, or even the secondary storage of the CPU. Distributed rendering using a cluster of workstations is the only way to navigate through such models. In this paper, we present a design of a distributed rendering system intended for massive models. Our design has a server that holds the skeleton of the whole model, namely, its scenegraph with actual geometry replaced by bounding boxes at all levels. The server divides the screen space among a number of clients and sends them a list of objects they need to render using a frustum culling step. The clients use 2 GPUs with one devoted to visibility culling and the other to rendering. Frustum culling at the server, visibility culling on one GPU, and rendering on the second GPU form the stages of our distributed rendering pipeline. We describe the design and implementation of our system and demonstrate the results of rendering relatively large models using different clusters of clients in this paper.

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“…A good survey on distributed rendering software was given by Ni et al [36] and Raffin et al [39]. Some of the best known software packages include CAVELib [8], VRjuggler [5], Syzygy [44], AnyScreen [10], OpenSG [40], Chromium [17], NAVER [18], Jinx [47] , and CGLX [11].…”

Section: Related Workmentioning

confidence: 99%

“…Our implementation of the volume renderer is based on CGLX [11], a Cross-Platform Cluster Graphic Library. CGLX was developed at the California Institute for Telecommunications and Information Technology at UC San Diego and is a flexible and transparent OpenGL-based graphics framework for distributed visualization systems in a master-slave setup.…”

Section: Cglxmentioning

confidence: 99%

Giga-Scale Multiresolution Volume Rendering on Distributed Display Clusters

Thelén

Meyer

Ebert

et al. 2011

Human Aspects of Visualization

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Abstract.Visualizing the enormous level of detail comprised in many of today's data sets is a challenging task and demands special processing techniques as well as a presentation on appropriate display devices. Desktop computers and laptops are often not suited for this task because data sets are simply too large and the limited screen size of these devices prevents users from perceiving the entire data set and severely restricts collaboration. Large high-resolution displays that combine the images of multiple smaller devices to form one large display area have proven to be an adequate solution to the ever-growing quantity of available data. The displays offer enough screen real estate to visualize such data sets entirely and facilitate collaboration, since multiple users are able to perceive the information at the same time. For an interactive visualization, the CPUs on the cluster driving the GPUs can be used to split up the computation of a scene into different areas, where each area is computed by a different rendering node.In this paper we focus on volumetric data sets and introduce a dynamic subdivision scheme incorporating multi-resolution wavelet representation to visualize data sets with several gigabytes of voxel data interactively on distributed rendering clusters. The approach makes efficient use of the resources available on modern graphics cards which mainly limit the amount of data that can be visualized. The implementation was successfully tested on a tiled display comprised of 25 compute nodes driving 50 LCD panels.

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CGLX: A Scalable, High-Performance Visualization Framework for Networked Display Environments

Cited by 86 publications

References 17 publications

Analyzing Google Earth application in a heterogeneous commodity cluster display wall

Analyzing Google Earth application in a heterogeneous commodity cluster display wall

Distributed massive model rendering

Giga-Scale Multiresolution Volume Rendering on Distributed Display Clusters

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