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Data-Intensive Supercomputing: The case for DISC
 

Summary: Data-Intensive Supercomputing:
The case for DISC
Randal E. Bryant
May 10, 2007
CMU-CS-07-128
School of Computer Science
Carnegie Mellon University
Pittsburgh, PA 15213
Abstract
Google and its competitors have created a new class of large-scale computer systems to support In-
ternet search. These "Data-Intensive Super Computing" (DISC) systems differ from conventional
supercomputers in their focus on data: they acquire and maintain continually changing data sets, in
addition to performing large-scale computations over the data. With the massive amounts of data
arising from such diverse sources as telescope imagery, medical records, online transaction records,
and web pages, DISC systems have the potential to achieve major advances in science, health care,
business efficiencies, and information access. DISC opens up many important research topics in
system design, resource management, programming models, parallel algorithms, and applications.
By engaging the academic research community in these issues, we can more systematically and in
a more open forum explore fundamental aspects of a societally important style of computing.
Keywords: parallel computing, data storage, web search

  

Source: Agrawal, Gagan - Department of Computer Science and Engineering, Ohio State University
Carnegie Mellon University, School of Computer Science
Reddy, Raj - School of Computer Science, Carnegie Mellon University

 

Collections: Computer Technologies and Information Sciences