A data distributed, parallel algorithm for ray-traced volume rendering
PRS '93 Proceedings of the 1993 symposium on Parallel rendering
Parallel volume-rendering algorithm performance on mesh-connected multicomputers
PRS '93 Proceedings of the 1993 symposium on Parallel rendering
A Sorting Classification of Parallel Rendering
IEEE Computer Graphics and Applications
Exploiting deep parallel memory hierarchies for ray casting volume rendering
PRS '97 Proceedings of the IEEE symposium on Parallel rendering
Design of dynamic load-balancing tools for parallel applications
Proceedings of the 14th international conference on Supercomputing
A Distributed Parallel Programming Framework
IEEE Transactions on Software Engineering
Scalable Rendering on PC Clusters
IEEE Computer Graphics and Applications
Parallel Volume Rendering Using PC Graphics Hardware
PG '01 Proceedings of the 9th Pacific Conference on Computer Graphics and Applications
Resource-Aware Scientific Computation on a Heterogeneous Cluster
Computing in Science and Engineering
Acceleration Techniques for GPU-based Volume Rendering
Proceedings of the 14th IEEE Visualization 2003 (VIS'03)
Resource-Aware Distributed Scheduling Strategies for Large-Scale Computational Cluster/Grid Systems
IEEE Transactions on Parallel and Distributed Systems
Dependency graph approach to load balancing distributed volume visualization
The Visual Computer: International Journal of Computer Graphics
Capacity-constrained point distributions: a variant of Lloyd's method
ACM SIGGRAPH 2009 papers
A Compute Unified System Architecture for Graphics Clusters Incorporating Data Locality
IEEE Transactions on Visualization and Computer Graphics
RenderAnts: interactive Reyes rendering on GPUs
ACM SIGGRAPH Asia 2009 papers
Parallel multiresolution volume rendering of large data sets with error-guided load balancing
EG PGV'04 Proceedings of the 5th Eurographics conference on Parallel Graphics and Visualization
Dynamic load balancing for parallel volume rendering
EG PGV'06 Proceedings of the 6th Eurographics conference on Parallel Graphics and Visualization
Optimized volume raycasting for graphics-hardware-based cluster systems
EG PGV'06 Proceedings of the 6th Eurographics conference on Parallel Graphics and Visualization
Parallel volume rendering on the IBM Blue Gene/P
EG PGV'08 Proceedings of the 8th Eurographics conference on Parallel Graphics and Visualization
Fast compositing for cluster-parallel rendering
EG PGV'10 Proceedings of the 10th Eurographics conference on Parallel Graphics and Visualization
EG PGV'10 Proceedings of the 10th Eurographics conference on Parallel Graphics and Visualization
A simple and flexible volume rendering framework for graphics-hardware-based raycasting
VG'05 Proceedings of the Fourth Eurographics / IEEE VGTC conference on Volume Graphics
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In interactive volume rendering, the cost for rendering a certain block of the volume strongly varies with dynamically changing parameters (most notably the camera position and orientation). In distributed environments — wherein each compute device renders one block — this potentially causes severe load-imbalance. Balancing the load usually induces costly data transfers causing critical rendering delays. In cases in which the sum of memory of all devices substantially exceeds the size of the data set, transfers can be reduced by storing data redundantly. We propose to partition the volume into many equally sized bricks and redundantly save them on different compute devices with the goal of being able to achieve evenly balanced load without any data transfers. The bricks assigned to a device are widely scattered throughout the volume. This minimizes the dependency on the view parameters, as the distribution of relatively cheap and expensive bricks stays roughly the same for most camera configurations. This again enables our fast and simple scheduler to evenly balance the load in almost any situation. In scenarios in which only very few bricks constitute the majority of the overall cost a brick can also be partitioned further and rendered by multiple devices.