Interactive Volume Exploration of Petascale Microscopy Data Streams Using a Visualization-Driven Virtual Memory Approach
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- Interactive Volume Exploration of Petascale Microscopy Data Streams Using a Visualization-Driven Virtual Memory Approach
- Hadwiger, Markus; Beyer, Johanna; Jeong, Won-Ki; Pfister, Hanspeter
- 2D images; Access time; Cache Miss; Data stream; High-resolution microscopy; High-throughput; Microscope images; Missing data; Multi resolution representation; Multi-resolutions; neuroscience; Out-of-core processing; Petascale; Raycasting; Virtual memory; Volume data; Volume visualization
- Issue Date
- IEEE COMPUTER SOC
- IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS, v.18, no.12, pp.2285 - 2294
- This paper presents the first volume visualization system that scales to petascale volumes imaged as a continuous stream of high-resolution electron microscopy images. Our architecture scales to dense, anisotropic petascale volumes because it: (1) decouples construction of the 3D multi-resolution representation required for visualization from data acquisition, and (2) decouples sample access time during ray-casting from the size of the multi-resolution hierarchy. Our system is designed around a scalable multi-resolution virtual memory architecture that handles missing data naturally, does not pre-compute any 3D multi-resolution representation such as an octree, and can accept a constant stream of 2D image tiles from the microscopes. A novelty of our system design is that it is visualization-driven: we restrict most computations to the visible volume data. Leveraging the virtual memory architecture, missing data are detected during volume ray-casting as cache misses, which are propagated backwards for on-demand out-of-core processing. 3D blocks of volume data are only constructed from 2D microscope image tiles when they have actually been accessed during ray-casting. We extensively evaluate our system design choices with respect to scalability and performance, compare to previous best-of-breed systems, and illustrate the effectiveness of our system for real microscopy data from neuroscience.
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