Display of Surfaces from Volume Data
IEEE Computer Graphics and Applications
Communication Costs for Parallel Volume-Rendering Algorithms
IEEE Computer Graphics and Applications
Parallel Volume Rendering Using Binary-Swap Compositing
IEEE Computer Graphics and Applications
A Parallel Visualization Pipeline for Terascale Earthquake Simulations
Proceedings of the 2004 ACM/IEEE conference on Supercomputing
Visualizing Very Large-Scale Earthquake Simulations
Proceedings of the 2003 ACM/IEEE conference on Supercomputing
Acceleration Techniques for GPU-based Volume Rendering
Proceedings of the 14th IEEE Visualization 2003 (VIS'03)
Hardware-Based Ray Casting for Tetrahedral Meshes
Proceedings of the 14th IEEE Visualization 2003 (VIS'03)
A parallel multiresolution volume rendering algorithm for large data visualization
Parallel Computing - Parallel graphics and visualization
MapReduce: simplified data processing on large clusters
OSDI'04 Proceedings of the 6th conference on Symposium on Opearting Systems Design & Implementation - Volume 6
Evaluating MapReduce for Multi-core and Multiprocessor Systems
HPCA '07 Proceedings of the 2007 IEEE 13th International Symposium on High Performance Computer Architecture
Scalable Parallel Programming with CUDA
Queue - GPU Computing
Massively parallel volume rendering using 2-3 swap image compositing
Proceedings of the 2008 ACM/IEEE conference on Supercomputing
Mars: a MapReduce framework on graphics processors
Proceedings of the 17th international conference on Parallel architectures and compilation techniques
Query-Driven Visualization of Time-Varying Adaptive Mesh Refinement Data
IEEE Transactions on Visualization and Computer Graphics
CellMR: A framework for supporting mapreduce on asymmetric cell-based clusters
IPDPS '09 Proceedings of the 2009 IEEE International Symposium on Parallel&Distributed Processing
Hadoop in Action
Interactive volume rendering of large sparse data sets using adaptive mesh refinement hierarchies
IEEE Transactions on Visualization and Computer Graphics
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
Parallel volume rendering on the IBM Blue Gene/P
EG PGV'08 Proceedings of the 8th Eurographics conference on Parallel Graphics and Visualization
A multiresolution volume rendering framework for large-scale time-varying data visualization
VG'05 Proceedings of the Fourth Eurographics / IEEE VGTC conference on Volume Graphics
More convenient more overhead: the performance evaluation of Hadoop streaming
Proceedings of the 2011 ACM Symposium on Research in Applied Computation
Many-Core architecture oriented parallel algorithm design for computer animation
MIG'11 Proceedings of the 4th international conference on Motion in Games
Interference-driven resource management for GPU-based heterogeneous clusters
Proceedings of the 21st international symposium on High-Performance Parallel and Distributed Computing
Tiled-MapReduce: Efficient and Flexible MapReduce Processing on Multicore with Tiling
ACM Transactions on Architecture and Code Optimization (TACO)
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In this paper we present a multi-GPU parallel volume rendering implemention built using the MapReduce programming model. We give implementation details of the library, including specific optimizations made for our rendering and compositing design. We analyze the theoretical peak performance and bottlenecks for all tasks required and show that our system significantly reduces computation as a bottleneck in the ray-casting phase. We demonstrate that our rendering speeds are adequate for interactive visualization (our system is capable of rendering a 10243 floating-point sampled volume in under one second using 8 GPUs), and that our system is capable of delivering both in-core and out-of-core visualizations. We argue that a multi-GPU MapReduce library is a good fit for parallel volume renderering because it is easy to program for, scales well, and eliminates the need to focus on I/O algorithms thus allowing the focus to be on visualization algorithms instead. We show that our system scales with respect to the size of the volume, and (given enough work) the number of GPUs.