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Computer system architectures which can support [[data parallel]] applications were promoted in the early 2000s for large-scale data processing requirements of data-intensive computing.<ref>[http://www.patrickpantel.com/download/papers/2004/kdd-msw04-1.pdf The terascale challenge] by D. Ravichandran, P. Pantel, and E. Hovy. "The terascale challenge," Proceedings of the KDD Workshop on Mining for and from the Semantic Web, 2004</ref> Data-parallelism applied computation independently to each data item of a set of data, which allows the degree of parallelism to be scaled with the volume of data. The most important reason for developing data-parallel applications is the potential for scalable performance, and may result in several orders of magnitude performance improvement. The key issues with developing applications using data-parallelism are the choice of the algorithm, the strategy for data decomposition, [[load balancing (computing)|load balancing]] on processing nodes, [[message passing]] communications between nodes, and the overall accuracy of the results.<ref>[http://www.cs.rochester.edu/u/umit/papers/ppopp01.ps Dynamic adaptation to available resources for parallel computing in an autonomous network of workstations] by U. Rencuzogullari, and S. Dwarkadas. "Dynamic adaptation to available resources for parallel computing in an autonomous network of workstations," Proceedings of the Eighth ACM SIGPLAN Symposium on Principles and Practices of Parallel Programming, 2001</ref> The development of a data parallel application can involve substantial programming complexity to define the problem in the context of available programming tools, and to address limitations of the target architecture. [[Information extraction]] from and indexing of Web documents is typical of data-intensive computing which can derive significant performance benefits from data parallel implementations since Web and other types of document collections can typically then be processed in parallel.<ref>[http://www.mathcs.emory.edu/~eugene/publications.html Information Extraction to Large Document Collections] by E. Agichtein, "Scaling Information Extraction to Large Document Collections," Microsoft Research, 2004</ref>
The US [[National Science Foundation]] (NSF) funded a research program from 2009 through 2010.<ref>{{Cite web |title= Data-intensive Computing |work= Program description |year= 2009 |publisher= NSF |url= http://www.nsf.gov/funding/pgm_summ.jsp?pims_id=503324&org=IIS |accessdate=24
* Approaches to [[parallel programming]] to address the [[Parallel computing|parallel processing]] of data on data-intensive systems
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* Identifying applications that can exploit this computing paradigm and determining how it should evolve to support emerging data-intensive applications
[[Pacific Northwest National Labs]] defined data-intensive computing as “capturing, managing, analyzing, and understanding data at volumes and rates that push the frontiers of current technologies”.<ref>[http://www.cs.cmu.edu/~bryant/presentations/DISC-concept.ppt Data Intensive Computing] by PNNL. "Data Intensive Computing," 2008</ref><ref>[http://www.computer.org/portal/web/csdl/doi/10.1109/MC.2009.26 The Changing Paradigm of Data-Intensive Computing] by R.T. Kouzes, G.A. Anderson, S.T. Elbert, I. Gorton, and D.K. Gracio, "The Changing Paradigm of Data-Intensive Computing," Computer, Vol. 42, No. 1, 2009, pp. 26-3</ref>
== Approach ==
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