DRONE: A Flexible Framework for Distributed Rendering and Display

Repplinger, Michael D.; Löffler, Alexander; Rubinstein, Dmitri; Slusallek, Philipp

doi:10.1007/978-3-642-10331-5_91

Cited by 11 publications

(4 citation statements)

References 7 publications

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“…The dataflow model is based on modules, each of them running on a processing unit, exchanging data through connections. DRONE [Repplinger et al 2009] relies on the Network-Integrated Multimedia Middleware (NMM) [Lohse et al 2008]. FlowVR Render [Allard and Raffin 2005] relies on FlowVR [Allard et al 2004] for the distributed rendering and display application.…”

Section: Task Parallelismsupporting

confidence: 46%

Many-core event evaluation

Marvie

Hirtzlin

Gautron

2013

Proceedings of the 18th International Conference on 3D Web Technology

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Figure 1: We introduce a novel event evaluation framework using task parallelism on multi/many-core CPU architectures for real-time animation of complex scenes. Our framework supports 5 dynamic scheduling strategies based on OpenMP and custom Pthread-based approaches. Parallel evaluation timings for an animation frame on a 6-core CPU are displayed for the implemented custom Pthread-based (left) and OpenMP-based (right) static schedulers for a population of 1K humanoids generating 110K events per frame. Each rectangle corresponds to the evaluation of a single scene graph node (bones transformations and skeletal constraint evaluation at left and right figure parts respectively). The color of each consecutive rectangle is modified automatically for readability purpose. The gaps correspond to the time spent either in sequential execution or idle. AbstractWe present a Many-Core Event Evaluation framework for real-time execution of many complex animation schemes applicable to a wide range of domains such as gaming and interactive pre-visualization in studio production. Our technique takes advantages of task parallelism on many-core CPU architecture using a two-level scheduling approach. Our generic approach can deal with tens of thousands event-processing nodes, event loops, non-deterministic and interaction-driven animation modifications at runtime. Versatility is further enforced through a native support of hierarchical animation graphs using prototypes and inline files. Our implementation based on the X3D event-based animation model exhibits performances approaching the theoretical upper bounds of parallelization.

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Section: Task Parallelismsupporting

confidence: 46%

Many-core event evaluation

Marvie

Hirtzlin

Gautron

2013

Proceedings of the 18th International Conference on 3D Web Technology

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“…Although remote shared visualization has been partially addressed through the Visualcasting extension [8] by Jeong, important issues such as multi-user interactions at the application level were not addressed. Repplinger et al proposed DRONE [11] as a flexible framework enabling distributed physically based rendering and supporting various deployment scenarios including remote multi-user visualization and interactions. However, as the project has become outdated, the implementation does not benefit from recent advances including modern cluster interconnections such as InfiniBand and new internet standards such as HTML5 and WebRTC opening the way to ubiquitous cross-platform solutions.…”

Section: Related Workmentioning

confidence: 99%

Multi-user predictive rendering on remote multi-GPU clusters

Randrianandrasana

Chanonier

Deleau

et al. 2018

2018 IEEE Fourth VR International Workshop on Collaborative Virtual Environments (3DCVE)

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Many stages of the industry workflow have been benefiting from CAD software applications and real-time computer graphics for decades allowing manufacturers to perform team project reviews and assessments while decreasing the need for expensive physical mockups. However, when it comes to the perceived quality of the final product, more sophisticated physically based engines are often preferred though involving huge computation times. In this context, our work aims at reducing this gap by providing a predictive rendering solution leveraging the computing resources offered by modern multi-GPU supercomputers. To that end, we propose a simple static load balancing approach leveraging the stochastic nature of Monte Carlo rendering. Our solution efficiently exploits the available computing resources and addresses the industry collaboration needs by providing a real-time multi-user web access to the virtual mockup.

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“…Although the presented distribution framework has been mainly developed for distributed stream processing on GPUs, the presented approach is not limited to GPUs. For example, the project Distributed Rendering Object Network (DRONE) 17 is also based on a simplified version of the presented framework. Here, the presented distribution framework is used for both rendering and display within Virtual Reality applications.…”

Section: A Generic Framework For Distributed Processingmentioning

confidence: 99%

“…The presentation block includes all the remaining nodes required to present rendered images. A detailed description about DRONE and its additional functionality can be found in ‘DRONE: a Flexible Framework for Distributed Rendering and Display’ 17.…”

Section: A Generic Framework For Distributed Processingmentioning

confidence: 99%

Stream processing on GPUs using distributed multimedia middleware

Repplinger

Slusallek

2010

Concurrency and Computation

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SUMMARYAvailable GPUs provide increasingly more processing power especially for multimedia and digital signal processing. Despite the tremendous progress in hardware and thus processing power, there are and always will be applications that require using multiple GPUs either running inside the same machine or distributed in the network due to computationally intensive processing algorithms. Existing solutions for developing applications for GPUs still require a lot of hand-optimization when using multiple GPUs inside the same machine and provide in general no or only limited support for using remote GPUs distributed in the network. In this paper we address this problem and show that an open distributed multimedia middleware, like the Network-Integrated Multimedia Middleware (NMM), is able (1) to seamlessly integrate processing components using GPUs, while completely hiding GPU-specific issues from the application developer, (2) to transparently combine processing components using GPUs or CPUs, and (3) to transparently use local and remote GPUs for distributed processing. Furthermore, we present a generic distribution framework to simplify the development of complex application scenarios.

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DRONE: A Flexible Framework for Distributed Rendering and Display

Cited by 11 publications

References 7 publications

Many-core event evaluation

Many-core event evaluation

Multi-user predictive rendering on remote multi-GPU clusters

Stream processing on GPUs using distributed multimedia middleware

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