Pixel-planes 5: a heterogeneous multiprocessor graphics system using processor-enhanced memories
SIGGRAPH '89 Proceedings of the 16th annual conference on Computer graphics and interactive techniques
Computer graphics: principles and practice (2nd ed.)
Computer graphics: principles and practice (2nd ed.)
PixelFlow: high-speed rendering using image composition
SIGGRAPH '92 Proceedings of the 19th annual conference on Computer graphics and interactive techniques
A Sorting Classification of Parallel Rendering
IEEE Computer Graphics and Applications
InfiniteReality: a real-time graphics system
Proceedings of the 24th annual conference on Computer graphics and interactive techniques
Load balancing for multi-projector rendering systems
HWWS '99 Proceedings of the ACM SIGGRAPH/EUROGRAPHICS workshop on Graphics hardware
QSplat: a multiresolution point rendering system for large meshes
Proceedings of the 27th annual conference on Computer graphics and interactive techniques
Hybrid sort-first and sort-last parallel rendering with a cluster of PCs
HWWS '00 Proceedings of the ACM SIGGRAPH/EUROGRAPHICS workshop on Graphics hardware
A Characterization of Ten Hidden-Surface Algorithms
ACM Computing Surveys (CSUR)
Illumination for computer generated pictures
Communications of the ACM
WireGL: a scalable graphics system for clusters
Proceedings of the 28th annual conference on Computer graphics and interactive techniques
Parallel rendering with k-way replication
PVG '01 Proceedings of the IEEE 2001 symposium on parallel and large-data visualization and graphics
Chromium: a stream-processing framework for interactive rendering on clusters
Proceedings of the 29th annual conference on Computer graphics and interactive techniques
Interactive visibility culling in complex environments using occlusion-switches
I3D '03 Proceedings of the 2003 symposium on Interactive 3D graphics
Image Layer Decomposition for Distributed Real-Time Rendering on Clusters
IPDPS '00 Proceedings of the 14th International Symposium on Parallel and Distributed Processing
Cg: a system for programming graphics hardware in a C-like language
ACM SIGGRAPH 2003 Papers
Brook for GPUs: stream computing on graphics hardware
ACM SIGGRAPH 2004 Papers
GPU Cluster for High Performance Computing
Proceedings of the 2004 ACM/IEEE conference on Supercomputing
Parallel occlusion culling on GPUs cluster
Proceedings of the 2006 ACM international conference on Virtual reality continuum and its applications
Parallel-SG: research of parallel graphics rendering system on PC-Cluster
Proceedings of the 2006 ACM international conference on Virtual reality continuum and its applications
HiPC'05 Proceedings of the 12th international conference on High Performance Computing
Streaming-enabled parallel dataflow architecture for multicore systems
EuroVis'10 Proceedings of the 12th Eurographics / IEEE - VGTC conference on Visualization
Hi-index | 0.00 |
Handling very large datasets has been a key problem addressed in real-time distributed rendering research. With the advent of the programmable Graphics Processing Unit (GPU), itis now possible and even profitable to move many application-specific computations to be carried out by the GPU. It has been shown that modern GPUs outperform the standard PC-platform CPUs on a broad class of computations by over a factor of seven. Given the low costs and high processing speeds of GPUs, there is a trend towards using clusters of CPU/GPU systems. Configuring and programming these clusters for efficient distribution ofdata and computations is a major challenge. What are the computations that can be offloaded from the CPU to a GPU? The answer to this question is not simple as it depends on the following four factors: GPU's processing capacity, GPU's internal bandwidth, GPU-CPU communication bandwidth and the external network bandwidth. All these factors are subjectto change with every generation of hardware. But additions and alternatives to the traditional data-parallel architectures are now needed to exploit the full capability of such clusters using functional parallelism. In this paper, we present a number of architectural configurations that could be adapted on such clusters. Specifically, we demonstrate use of one such architecture: application of a GPU-based pipelined architecture to our work on real-time processing and rendering of large-point datasets which demands complex computations. We have also introduced a list of application and system parameters that are necessary to determine an optimal distribution of computation on the GPUs of a graphics cluster.