Chromium: a stream-processing framework for interactive rendering on clusters

By Greg Humphreys, M. Houston, Ren Ng, R. Frank, Sean Ahern, P. Kirchner, James T. Klosowski. International Conference on Computer Graphics and Interactive Techniques, 2002

733 citations recorded by the source corpus.

Subfields: Computer Graphics and Computer-Aided Design, Computer Vision and Pattern Recognition, Hardware and Architecture

View Paper: https://www.semanticscholar.org/paper/f9b1a799fe1ad643caef75f764047472e18059a3

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Citations in the corpus

3D TV: a scalable system for real-time acquisition, transmission, and autostereoscopic display of dynamic scenes (2004, methodology): “Graphics rendering for multi-projector systems can be efficiently parallelized on clusters of PCs using, for example, the Chromium API [Humphreys et al. 2002].”
First steps towards handheld augmented reality (2003, methodology): “Rather than receiving 2D drawing instructions such as via the VNC protocol [11], a 3D command streaming protocol like Chromium [ 10 ] can be used.”
Data streams: algorithms and applications (2005, background): “See [38] for an example, [39] for a sui t ble model, and [82] for stream-related results.”
vCUDA: GPU accelerated high performance computing in virtual machines (2009, background): “Chromium provides a mechanism for implementing plugin modules that alter the stream of GL commands, allowing the distribute parallel rendering.”
GPU Cluster for High Performance Computing (2004, background): “Although there have been some efforts to exploit the parallelism of a graphics PC cluster for interactive graphics tasks [9, 13, 14], to the best of our knowledge we are the first to develop a scalable GPU cluster for high performance scientific computing and large-scale simulation.”
rCUDA: Reducing the number of GPU-based accelerators in high performance clusters (2010, background): “Therefore, as there is no low-level standard interface to drive GPUs, the virtualization boundary has to be placed at the high-level standard APIs, thus leading to different strategies for virtualizing GPUs depending on the target usage.”
Developing serious games for cultural heritage: a state-of-the-art review (2010, background): “If too much is visible at one time for this to be possible, a cluster of computers may be used, where the entire scene is often too large for a single computer to hold in memory but is able to be distributed among the cluster with the computers’ individual renders being accumulated and composited to”