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1 - Data-Intensive Computing: A Challenge for the 21st Century

Published online by Cambridge University Press:  05 December 2012

By
Ian Gorton  and
Deborah K. Gracio
Edited by
Ian Gorton  and
Deborah K. Gracio
Ian Gorton
Affiliation:
Pacific Northwest National Laboratory
Deborah K. Gracio
Affiliation:
Pacific Northwest National Laboratory
Ian Gorton
Affiliation:
Pacific Northwest National Laboratory, Washington
Deborah K. Gracio
Affiliation:
Pacific Northwest National Laboratory, Washington
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Summary

Introduction

In our world of rapid technological change, occasionally it is instructive to contemplate how much has altered in the last few years. Remembering life without the ability to view the World Wide Web (WWW) through browser windows will be difficult, if not impossible, for less “mature” readers. Is it only seven years since YouTube first appeared, a Web site that is now ingrained in many facets of modern life? How did we survive without Facebook all those (actually, about five) years ago?

In 2010, various estimates put the amount of data stored by consumers and businesses around the world in the vicinity of 13 exabytes, with a growth rate of 20 to 25 percent per annum. That is a lot of data. No wonder IBM is pursuing building a 120-petabyte storage array. Obviously there is going to be a market for such devices in the future. As data volumes of all types – from video and photos to text documents and binary files for science – continue to grow in number and resolution, it is clear that we have genuinely entered the realm of data-intensive computing, or as it is often now referred to, big data.

Interestingly, the term “data-intensive computing” was actually coined by the scientific community. Traditionally, scientific codes have been starved of sufficient compute cycles, a paucity that has driven the creation of ever larger and faster high-performance computing machines, typically known as supercomputers. The Top 500 Web site shows the latest benchmark results that characterize the fastest supercomputers on the planet.

Type
Chapter
Information
Data-Intensive Computing
Architectures, Algorithms, and Applications
 , pp. 1 - 11
Publisher: Cambridge University Press
Print publication year: 2012

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References

1. Johnston, W. “High-Speed, Wide Area, Data Intensive Computing: A Ten Year Retrospective.” Presented at 7th IEEE Symposium on High Performance Distributed Computing, Chicago, July 1998.Google Scholar
2. Hey, A. J. G. and Trefethen, A. E. “The Data Deluge: An e-Science Perspective.” In Berman, F., Fox, G. C. and Hey, A. J. G. (eds.), Grid Computing – Making the Global Infrastructure a Reality. 809–24. Wiley and Sons, 2003. From http://eprints.soton.ac.uk/257648/.Google Scholar
3. Bell, G., Gray, J., and Szalay, A. “Petascale Computational Systems.” Computer 39, no. 1 (2006): 110–12.CrossRefGoogle Scholar
4. Newman, H. B., Ellisman, M. H., and Orcutt, J. A. 2003. “Data-Intensive e-Science Frontier Research.” Commun. ACM 46, no. 11 (Nov. 2003): 68–77.CrossRefGoogle Scholar
5. Dean, J., and Ghemawat, S. “MapReduce: Simplified Data Processing on Large Clusters.” Commun. ACM 51, no. 1 (Jan. 2008): 107–13.CrossRefGoogle Scholar
6. Stanzione, Dan. “The iPlant Collaborative: Cyberinfrastructure to Feed the World,” IEEE Computer (Nov. 2011), 44–52.Google Scholar

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