Data-Intensive Supercomputing in the Cloud: Global Analytics for Satellite Imagery

@article{Warren2016DataIntensiveSI,
  title={Data-Intensive Supercomputing in the Cloud: Global Analytics for Satellite Imagery},
  author={Michael S. Warren and S. Skillman and R. Chartrand and T. Kelton and R. Keisler and D. Raleigh and Matthew J. Turk},
  journal={2016 Seventh International Workshop on Data-Intensive Computing in the Clouds (DataCloud)},
  year={2016},
  pages={24-31}
}
  • Michael S. Warren, S. Skillman, +4 authors Matthew J. Turk
  • Published 2016
  • Computer Science
  • 2016 Seventh International Workshop on Data-Intensive Computing in the Clouds (DataCloud)
  • We present our experiences using cloud computing to support data-intensive analytics on satellite imagery for commercial applications. Drawing from our background in highperformance computing, we draw parallels between the early days of clustered computing systems and the current state of cloud computing and its potential to disrupt the HPC market. Using our own virtual file system layer on top of cloud remote object storage, we demonstrate aggregate read bandwidth of 230 gigabytes per second… CONTINUE READING
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    References

    SHOWING 1-10 OF 24 REFERENCES
    Seeing the Earth in the Cloud: Processing one petabyte of satellite imagery in one day
    • 16
    BEOWULF: A Parallel Workstation for Scientific Computation
    • 755
    • PDF
    UMN-MapServer: A High-Performance, Interoperable, and Open Source Web Mapping and Geo-spatial Analysis System
    • 55
    A case for NOW (networks of workstation)
    • 78
    • PDF
    Parallel Supercomputing with Commodity Components
    • 51
    • PDF
    Implementation on Landsat Data of a Simple Cloud-Mask Algorithm Developed for MODIS Land Bands
    • 59
    • PDF
    Hadoop: The Definitive Guide
    • 3,881
    • PDF
    A Case for NOW (Networks Of Workstations)
    • 959
    • PDF
    Dark Sky Simulations: Early Data Release
    • 105
    • PDF
    Avalon: an Alpha/Linux cluster achieves 10 Gflops for $15k
    • 46