Parallel automatic registration of large scale microscopic images on multiprocessor CPUs and GPUs

Lee Alex Donald Cooper*, Kun Huang, Manuel Ujaldon

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations

Abstract

During the present decade, emerging architectures like multicore CPUs and graphics processing units (GPUs) have steadily gained popularity for their ability to deploy high computational power at a low cost. In this paper, we combine parallelization techniques on a cooperative cluster of multicore CPUs and multisocket GPUs to apply their joint computational power to an automatic image registration algorithm intended for the analysis of high-resolution microscope images. Registration methods pose a computational challenge within the biomedical field due to the large size of microscope image data sets, which typically extend to the Terabyte scale. We analyze this application to identify those parts which are more favorable to the CPU and GPU execution models and decompose the process accordingly. Performance results are presented for two sets of images: mouse placenta (16K × 16K pixels) and mouse mammary tumor (23K × 62K pixels). Execution times are shown on different multi-node, multi-socket and multi-core configurations to provide performance insights about the most effective approach.

Original languageEnglish (US)
Title of host publication2011 IEEE International Symposium on Parallel and Distributed Processing, Workshops and Phd Forum, IPDPSW 2011
Pages1367-1376
Number of pages10
DOIs
StatePublished - Dec 20 2011
Event25th IEEE International Parallel and Distributed Processing Symposium, Workshops and Phd Forum, IPDPSW 2011 - Anchorage, AK, United States
Duration: May 16 2011May 20 2011

Publication series

NameIEEE International Symposium on Parallel and Distributed Processing Workshops and Phd Forum

Other

Other25th IEEE International Parallel and Distributed Processing Symposium, Workshops and Phd Forum, IPDPSW 2011
CountryUnited States
CityAnchorage, AK
Period5/16/115/20/11

Keywords

  • CUDA
  • GPU
  • Heterogeneous computing
  • Massively parallel architectures
  • Registration on large-scale images

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Software
  • Theoretical Computer Science

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