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Metacomputing and data-intensive applications

  • Paul Messina
Session B-4. Novel Distributed Applications
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1274)

Abstract

Metacomputing-the concurrent use of multiple networklinked computers for solving application problems-is gaining increasing popularity. Much of the early work in metacomputing focused on harnessing greater processing power than could be found at a single site or on combining heterogeneous computer architectures to exploit the best features of each for a given problem. Another compelling rationale for using metacomputing is the acquisition, manipulation, and analysis of data that are stored remotely or acquired by distant instruments. When the data to be accessed and processed are voluminous, we refer to the applications as data intensive.

Large-scale projects of this kind require leading-edge technologies in multiple fields: high-speed networking, high-performance computing, software or interactive control of programs and instruments, and data storage facilities.

This paper describes three Caltech projects that take advantage of metacomputing, are data-intensive, or both. Brief mention is also made of a few additional projects that have these characteristics and are being conducted elsewhere. The applications described include synthetic aperture radar (SAR) data processing; a user interface for controlling the access and processing of scientific data and the use of distributed archives and computers for that data; and large-scale distributed interactive simulations for synthetic theater of war. Since Caltech is not the only institution that is pursuing distributed, data-intensive metacomputing, we also provide examples of interesting projects from other sites, such as telemi croscopy at the University of California at San Diego and the Distributed Object Computation Testbed that is being developed at the San Diego Supercomputer Center.

The paper ends with a brief survey of future projects in this area and some developments that will enable the use of metacomputing to more and more distributed, data-intensive applications.

Keywords

Synthetic Aperture Radar Synthetic Aperture Radar Image Synthetic Aperture Radar Data Compelling Rationale Tilt Series 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 1997

Authors and Affiliations

  • Paul Messina
    • 1
  1. 1.California Institute of TechnologyPasadenaUSA

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