Efficient ray tracing of volume data
ACM Transactions on Graphics (TOG)
Segmented ray casting for data parallel volume rendering
PRS '93 Proceedings of the 1993 symposium on Parallel rendering
A data distributed, parallel algorithm for ray-traced volume rendering
PRS '93 Proceedings of the 1993 symposium on Parallel rendering
A Sorting Classification of Parallel Rendering
IEEE Computer Graphics and Applications
Accelerated volume rendering and tomographic reconstruction using texture mapping hardware
VVS '94 Proceedings of the 1994 symposium on Volume visualization
Efficiently using graphics hardware in volume rendering applications
Proceedings of the 25th annual conference on Computer graphics and interactive techniques
SIGGRAPH '88 Proceedings of the 15th annual conference on Computer graphics and interactive techniques
Sort-last parallel rendering for viewing extremely large data sets on tile displays
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
Optical Models for Direct Volume Rendering
IEEE Transactions on Visualization and Computer Graphics
Parallel Volume Rendering Using Binary-Swap Compositing
IEEE Computer Graphics and Applications
SIGGRAPH '84 Proceedings of the 11th annual conference on Computer graphics and interactive techniques
Accelerating Volume Reconstruction With 3D Texture Hardware
Accelerating Volume Reconstruction With 3D Texture Hardware
Acceleration Techniques for GPU-based Volume Rendering
Proceedings of the 14th IEEE Visualization 2003 (VIS'03)
End-to-End Study of Parallel Volume Rendering on the IBM Blue Gene/P
ICPP '09 Proceedings of the 2009 International Conference on Parallel Processing
Hierarchical visualization and compression of large volume datasets using GPU clusters
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
A scalable, hybrid scheme for volume rendering massive data sets
EG PGV'06 Proceedings of the 6th Eurographics conference on Parallel Graphics and Visualization
Multi-GPU sort-last volume visualization
EG PGV'08 Proceedings of the 8th 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
MPI-hybrid parallelism for volume rendering on large, multi-core systems
EG PGV'10 Proceedings of the 10th Eurographics conference on Parallel Graphics and Visualization
Proceedings of the 2012 International Workshop on Programming Models and Applications for Multicores and Manycores
Cross-segment load balancing in parallel rendering
EG PGV'11 Proceedings of the 11th Eurographics conference on Parallel Graphics and Visualization
Multi-core and many-core shared-memory parallel raycasting volume rendering optimization and tuning
International Journal of High Performance Computing Applications
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Data sets of immense size are regularly generated on large scale computing resources. Even among more traditional methods for acquisition of volume data, such as MRI and CT scanners, data which is too large to be effectively visualized on standard workstations is now commonplace. One solution to this problem is to employ a 'visualization cluster,' a small to medium scale cluster dedicated to performing visualization and analysis of massive data sets generated on larger scale supercomputers. These clusters are designed to fit a different need than traditional supercomputers, and therefore their design mandates different hardware choices, such as increased memory, and more recently, graphics processing units (GPUs). While there has been much previous work on distributed memory visualization as well as GPU visualization, there is a relative dearth of algorithms which effectively use GPUs at a large scale in a distributed memory environment. In this work, we study a common visualization technique in a GPU-accelerated, distributed memory setting, and present performance characteristics when scaling to extremely large data sets.