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ISP: an optimal out-of-core image-set processing streaming architecture for parallel heterogeneous systems
Linh Khanh Ha1, Jens Krüger, João Luiz Dihl Comba
1Scientific Imaging and Computing Institute, University of Utah, 72 S Central Campus Dr, WEB, Room 3692, Salt Lake City, UT 84112, USA. lha@sci.utah.edu
IEEE Transactions on Visualization and Computer Graphics
|February 1, 2012
Summary
Image population analysis is computationally intensive. We introduce ISP, a streaming framework using commodity hardware to efficiently process large image datasets, matching supercomputer performance without high costs.
Area of Science:
- Computational Biology
- Bioinformatics
- Image Analysis
Background:
- Image population analysis is crucial for understanding biological systems.
- High computational demands and memory requirements limit its application.
- Access to supercomputing resources is often restricted.
Purpose of the Study:
- To introduce ISP, a novel Image-Set Processing streaming framework.
- To address computational challenges in large-scale image population analysis.
- To enable efficient processing on commodity heterogeneous CPU/GPU systems.
Main Methods:
- Developed specialized streaming algorithms and data structures for out-of-core processing.
- Utilized asynchronous execution on parallel heterogeneous systems.
- Implemented a framework to hide processing pipeline latency.
Main Results:
- ISP provides an optimal solution for out-of-core multi-image processing.
- Achieved high memory usage and computational efficiency.
- Demonstrated comparable performance to in-core solutions for intensive tasks.
Conclusions:
- ISP effectively overcomes computational limitations in image population analysis.
- The framework democratizes access to powerful image processing capabilities.
- ISP is efficient on both synthetic and real-world datasets.
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