High performance visualization of time-varying volume data over a wide-area network status
Proceedings of the 2000 ACM/IEEE conference on Supercomputing
SIGGRAPH '88 Proceedings of the 15th annual conference on Computer graphics and interactive techniques
A study of I/O methods for parallel visualization of large-scale data
Parallel Computing - Parallel graphics and visualization
Optical Flow Computation on Compute Unified Device Architecture
ICIAP '07 Proceedings of the 14th International Conference on Image Analysis and Processing
Bandwidth intensive 3-D FFT kernel for GPUs using CUDA
Proceedings of the 2008 ACM/IEEE conference on Supercomputing
Compute Unified Device Architecture Application Suitability
Computing in Science and Engineering
Accelerating geoscience and engineering system simulations on graphics hardware
Computers & Geosciences
Computing and Visualization in Science
Multi-GPU volume rendering using MapReduce
Proceedings of the 19th ACM International Symposium on High Performance Distributed Computing
Large data visualization on distributed memory multi-GPU clusters
Proceedings of the Conference on High Performance Graphics
Sort-First Parallel Volume Rendering
IEEE Transactions on Visualization and Computer Graphics
GPU-based cloud performance for LiDAR data processing
Proceedings of the 2nd International Conference on Computing for Geospatial Research & Applications
A CPU-GPU hybrid approach for the unsymmetric multifrontal method
Parallel Computing
Hybrid Parallelism for Volume Rendering on Large-, Multi-, and Many-Core Systems
IEEE Transactions on Visualization and Computer Graphics
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Visualizing 3D/4D environmental data is critical to understanding and predicting environmental phenomena for relevant decision making. This research explores how to best utilize graphics process units (GPUs) and central processing units (CPUs) collaboratively to speed up a generic geovisualization process. Taking the visualization of dust storms as an example, we developed a systematic 3D/4D geovisualization framework including preprocessing, coordinate transformation interpolation, and rendering. To compare the potential speedup of using GPUs versus that of using CPUs, we have implemented visualization components based on both multi-core CPUs and many-core GPUs. We found that (1) multi-core CPUs and many-core GPUs can improve the efficiency of mathematical calculations and rendering using multithreading techniques; (2) given the same amount of data, when increasing the size of blocks of GPUs for coordinate transformation, the executing time of interpolation and rendering drops consistently after reaching a peak; (3) the best performances obtained by GPU-based implementations in all the three major processes, are usually faster than CPU-based implementations whereas the best performance of rendering with GPUs is very close to that with CPUs; and (4) as the GPU on-board memory limits the capabilities of processing large volume data, preprocessing data with CPUs is necessary when visualizing large volume data which exceed the on-board memory of GPUs. However, the efficiency may be significantly hampered by the relative high-latency of the data exchange between CPUs and GPUs. Therefore, visualization of median size 3D/4D environmental data using GPUs is a better solution than that of using CPUs.