ISP: An optimal out-of-core image-set processing streaming architecture for parallel heterogeneous systems

Linh K. Ha, Jens Krüger, João L D Comba, Cláudio T. Silva, Sarang Joshi

Research output: Contribution to journalArticle

Abstract

Image population analysis is the class of statistical methods that plays a central role in understanding the development, evolution, and disease of a population. However, these techniques often require excessive computational power and memory that are compounded with a large number of volumetric inputs. Restricted access to supercomputing power limits its influence in general research and practical applications. In this paper we introduce ISP, an Image-Set Processing streaming framework that harnesses the processing power of commodity heterogeneous CPU/GPU systems and attempts to solve this computational problem. In ISP, we introduce specially designed streaming algorithms and data structures that provide an optimal solution for out-of-core multiimage processing problems both in terms of memory usage and computational efficiency. ISP makes use of the asynchronous execution mechanism supported by parallel heterogeneous systems to efficiently hide the inherent latency of the processing pipeline of out-of-core approaches. Consequently, with computationally intensive problems, the ISP out-of-core solution can achieve the same performance as the in-core solution. We demonstrate the efficiency of the ISP framework on synthetic and real datasets.

Original languageEnglish (US)
Article number6144060
Pages (from-to)838-851
Number of pages14
JournalIEEE Transactions on Visualization and Computer Graphics
Volume18
Issue number6
DOIs
StatePublished - 2012

Keywords

  • GPUs
  • atlas construction
  • diffeomorphism
  • multiimage processing framework
  • out-of-core processing

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Computer Graphics and Computer-Aided Design

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