Spatial prefetching for out-of-core visualization of multidimensional data

Lipsa, Dan; Rhodes, Philip J.; Sparr, Ted M.

doi:10.1117/12.704751

Cited by 3 publications

(6 citation statements)

References 12 publications

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“…As the algorithm moves through the image, so does the sliding window. Regions of the image no longer covered are discarded and we make new areas of the cache available to be filled with data from disk, in contrast to the LRU discarding approach adopted by Lipsa et al 17 The most important point in our strategy is that filling the cache is always an asynchronous operation, performed in a separate thread of execution.…”

Section: The Sliding Window Approachmentioning

confidence: 99%

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Seeing through the window: pre-fetching strategies for out-of-core image processing algorithms

2008

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Scientific data files have been increasing in size during the past decades. In the medical field, for instance, magnetic resonance imaging and computer aided tomography can yield image volumes of several gigabytes. While secondary storage (hard disks) increases in capacity and its cost per megabyte slumps over the years, primary memory (RAM) can still be a bottleneck in the processing of huge amounts of data. This represents a problem for image processing algorithms, which often need to keep in memory the original image and a copy of it to store the results. Operating systems optimize memory usage with memory paging and enhanced I/O operations. Although image processing algorithms usually work on neighbouring areas of a pixel, they follow pre-determined paths through the image and might not benefit from the memory paging strategies offered by the operating system, which are general purpose and unidimensional. Having the principles of locality and predetermined traversal paths in mind, we developed an algorithm that uses multi-threaded pre-fetching of data to build a disk cache in memory. Using the concept of a window that slides over the data, we predict the next block of memory to be read according to the path followed by the algorithm and asynchronously pre-fetch such block before it is actually requested. While other out-of-core techniques reorganize the original file in order to optimize reading, we work directly on the original file. We demonstrate our approach in different applications, each with its own traversal strategy and sliding window structure.

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Section: The Sliding Window Approachmentioning

confidence: 99%

“…Lipsa et al focussed on visualisation algorithms as well. 17 They did not reformat the original file either and used the concept of a multidimensional cache block that was filled with data as pixels were requested. When the cache was full, cache blocks were discarded according to the least recently used (LRU) approach.…”

Section: Introductionmentioning

confidence: 99%

Seeing through the window: pre-fetching strategies for out-of-core image processing algorithms

2008

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“…We use dynamic chunking [1] to speed-up out-of-core execution for two visualization applications using four different data access patterns. One pattern is implemented in an arbitrary direction slicer application that reads slices from a volume of data and composes them to build a maximum intensity projection (MIP) [2] representation of the volume.…”

Section: Dynamic Chunking Validationmentioning

confidence: 99%

“…Previously, dynamic chunking was described in the context of a slicer visualization application [1] and it was shown that it provides some of the benefits of file chunking without having to reorganize or maintain multiple copies of the file. In this paper we extend this work to apply to an arbitrary direction slicer and to ray casting.…”

Section: Introductionmentioning

confidence: 99%

“…Given the size of today's data, out-of-core visualization techniques are increasingly important in many domains of scientific research. In earlier work a technique called dynamic chunking [1] was proposed that can provide significant performance improvements for an out-of-core, arbitrary direction slicer application. In this work we validate dynamic chunking for several common data access patterns used in volume visualization applications.…”

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confidence: 99%

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Dynamic Chunking for Out-of-Core Volume Visualization Applications

Lipsa

Sparr

Laramee

2009

Advances in Visual Computing

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Abstract. Given the size of today's data, out-of-core visualization techniques are increasingly important in many domains of scientific research. In earlier work a technique called dynamic chunking [1] was proposed that can provide significant performance improvements for an out-of-core, arbitrary direction slicer application. In this work we validate dynamic chunking for several common data access patterns used in volume visualization applications. We propose optimizations that take advantage of extra knowledge about how data is accessed or knowledge about the behavior of previous iterations and can significantly improve performance. We present experimental results that show that dynamic chunking has performance close to regular chunking but has the added advantage that no reorganization of data is required. Dynamic chunking with the proposed optimizations can be significantly faster on average than chunking for certain common data access patterns.

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Enabling the interactive display of large medical volume datasets by multiresolution bricking

et al. 2009

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In this paper, we present an approach to interactive out-of-core volume data exploration that has been developed to augment the existing capabilities of the LhpBuilder software, a core component of the European project LHDL (http://www.biomedtown.org/biomed_town/lhdl). The requirements relate to importing, accessing, visualizing and extracting a part of a very large volume dataset by interactive visual exploration. Such datasets contain billions of voxels and, therefore, several gigabytes are required just to store them, which quickly surpass the virtual address limit of current 32-bit PC platforms. We have implemented a hierarchical, bricked, partition-based, out-of-core strategy to balance the usage of main and external memories. A new indexing scheme is introduced, which permits the use of a multiresolution bricked volume layout with minimum overhead and also supports fast data compression. Using the hierarchy constructed in a pre-processing step, we generate a coarse approximation that provides a preview using direct volume visualization for large-scale datasets. A user can interactively explore the dataset by specifying a region of interest (ROI), which further generates a much more accurate data representation inside the ROI. If even more precise accuracy is needed inside the ROI, nested ROIs are used. The software has been constructed using the Multimod Application Framework, a VTK-based system; however, the approach can be adopted for the other systems in a straightforward way. Experimental results show that the user can interactively explore large volume datasets such as the Visible Human Male/Female (with

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Spatial prefetching for out-of-core visualization of multidimensional data

Cited by 3 publications

References 12 publications

Seeing through the window: pre-fetching strategies for out-of-core image processing algorithms

Seeing through the window: pre-fetching strategies for out-of-core image processing algorithms

Dynamic Chunking for Out-of-Core Volume Visualization Applications

Enabling the interactive display of large medical volume datasets by multiresolution bricking

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