Net Juggler: running VR Juggler with multiple displays on a commodity component cluster

Allard, Jérémie; Gouranton, Valérie; Lecointre, Loïck; Melin, Emmanuel; Raffin, Bruno

doi:10.1109/vr.2002.996534

Cited by 29 publications

(19 citation statements)

References 5 publications

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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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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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“…Its main aim is to make virtual reality configurations easy to set up and use without the need to know details about the devices and hardware configuration, but not specifically to provide scalable parallel rendering. Extensions of VR Juggler, such as for example ClusterJuggler [7] and NetJuggler [4], are typically based on the replication of application and data on each cluster node and basically take care of synchronization issues, but fail to provide a flexible and powerful configuration mechanism that efficiently supports scalable rendering as also noted in [51]. The presented system is different from VR Juggler in that it fully supports scalable parallel rendering such as sort-first and sort-last task decomposition and image compositing, it provides more flexible node configurations which for example allow specifying arbitrary task decomposition and image compositing combinations as simple compound layouts.…”

Section: Generic Approachesmentioning

confidence: 99%

Equalizer: A Scalable Parallel Rendering Framework

Eilemann

Makhinya

Pajarola

2009

IEEE Trans. Visual. Comput. Graphics

141

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Abstract-Continuing improvements in CPU and GPU performances as well as increasing multi-core processor and cluster-based parallelism demand for flexible and scalable parallel rendering solutions that can exploit multipipe hardware accelerated graphics. In fact, to achieve interactive visualization, scalable rendering systems are essential to cope with the rapid growth of data sets. However, parallel rendering systems are non-trivial to develop and often only application specific implementations have been proposed. The task of developing a scalable parallel rendering framework is even more difficult if it should be generic to support various types of data and visualization applications, and at the same time work efficiently on a cluster with distributed graphics cards. In this paper we introduce a novel system called Equalizer, a toolkit for scalable parallel rendering based on OpenGL which provides an application programming interface (API) to develop scalable graphics applications for a wide range of systems ranging from large distributed visualization clusters and multi-processor multipipe graphics systems to single-processor single-pipe desktop machines. We describe the system architecture, the basic API, discuss its advantadges over previous approaches, present example configurations and usage scenarios as well as scalability results.

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“…Each process has identical behavior and manages the entire dataset but renders only a certain portion of it. Representative examples of this approach are VR Juggler [13] and Net Juggler [14]. In the client-server setup, instead, a server distributes appropriate data to a number of rendering nodes and handles the synchronization among them.…”

Section: Related Workmentioning

confidence: 99%

Scalable rendering of massive triangle meshes on light field displays

Bettio

Gobbetti

Marton

et al. 2008

Computers & Graphics

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We report on a multiresolution rendering system driving light field displays based on a specially arranged array of projectors and a holographic screen. The system gives multiple freely moving naked-eye viewers the illusion of seeing and manipulating 3D objects with continuous viewer-independent parallax. Multi-resolution techniques which take into account the displayed light field geometry are employed to dynamically adapt model resolution to display capabilities and timing constraints. The approach is demonstrated on two different scales: a desktop PC driving a 7.4Mbeams TV-size display, and a cluster-parallel solution driving a large (1.6x0.9 meters) 35Mbeams display which supports a room-size working space. In both cases, massive meshes of tens of millions of triangles are manipulated at interactive rates.

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Net Juggler: running VR Juggler with multiple displays on a commodity component cluster

Abstract: Abstract

Cited by 29 publications

References 5 publications

Distributed massive model rendering

Distributed massive model rendering

Equalizer: A Scalable Parallel Rendering Framework

Scalable rendering of massive triangle meshes on light field displays

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