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The rapid growth of the [[Internet]] and [[World Wide Web]] led to vast amounts of information available online. In addition, business and government organizations create large amounts of both structured and [[unstructured information]], which need to be processed, analyzed, and linked. [[Vinton Cerf]] described this as an “information avalanche” and stated, “we must harness the Internet’s energy before the information it has unleashed buries us”.<ref>[http://research.google.com/pubs/author32412.html An Information Avalanche], by Vinton Cerf, IEEE Computer, Vol. 40, No. 1, 2007, pp. 104-105.</ref> An [[International Data Corporation|IDC]] white paper sponsored by [[EMC Corporation]] estimated the amount of information currently stored in a digital form in 2007 at 281 exabytes and the overall compound growth rate at 57% with information in organizations growing at even a faster rate.<ref>[http://www.emc.com/collateral/analyst-reports/expanding-digital-idc-white-paper.pdf The Expanding Digital Universe] {{webarchive |url=https://web.archive.org/web/20130627193204/http://www.emc.com/collateral/analyst-reports/expanding-digital-idc-white-paper.pdf |date=June 27, 2013 }}, by J.F. Gantz, D. Reinsel, C. Chute, W. Schlichting, J. McArthur, S. Minton, J. Xheneti, A. Toncheva, and A. Manfrediz, [[International Data Corporation|IDC]], White Paper, 2007.</ref> In a 2003 study of the so-called information explosion it was estimated that 95% of all current information exists in unstructured form with increased data processing requirements compared to structured information.<ref>[http://www2.sims.berkeley.edu/research/projects/how-much-info-2003/ How Much Information? 2003], by P. Lyman, and H.R. Varian, University of California at Berkeley, Research Report, 2003.</ref> The storing, managing, accessing, and processing of this vast amount of data represents a fundamental need and an immense challenge in order to satisfy needs to search, analyze, mine, and visualize this data as information.<ref>[http://www.sdsc.edu/about/director/pubs/communications200812-DataDeluge.pdf Got Data? A Guide to Data Preservation in the Information Age] {{Webarchive|url=https://web.archive.org/web/20110718061155/http://www.sdsc.edu/about/director/pubs/communications200812-DataDeluge.pdf |date=2011-07-18 }}, by F. Berman, Communications of the ACM, Vol. 51, No. 12, 2008, pp. 50-56.</ref> Data-intensive computing is intended to address this need.
[[Parallel computing|Parallel processing]] approaches can be generally classified as either ''compute-intensive'', or ''data-intensive''.<ref>[http://portal.acm.org/citation.cfm?id=280278 Models and languages for parallel computation], by D.B. Skillicorn, and D. Talia, ACM Computing Surveys, Vol. 30, No. 2, 1998, pp. 123-169.</ref><ref name=":0">[http://www.pnl.gov/science/images/highlights/computing/dic_special.pdfData-Intensive Computing in the 21st Century]{{Dead link|date=July 2019 |bot=InternetArchiveBot |fix-attempted=yes }}, by I. Gorton, P. Greenfield, A. Szalay, and R. Williams, IEEE Computer, Vol. 41, No. 4, 2008, pp. 30-32.</ref><ref>[http://www.computer.org/portal/web/csdl/doi/10.1109/MC.2008.122 High-Speed, Wide Area, Data Intensive Computing: A Ten Year Retrospective], by W.E. Johnston, IEEE Computer Society, 1998.</ref> Compute-intensive is used to describe application programs that are compute-bound. Such applications devote most of their execution time to computational requirements as opposed to I/O, and typically require small volumes of data. Parallel processing of compute-intensive applications typically involves parallelizing individual algorithms within an application process, and decomposing the overall application process into separate tasks, which can then be executed in parallel on an appropriate computing platform to achieve overall higher performance than serial processing. In compute-intensive applications, multiple operations are performed simultaneously, with each operation addressing a particular part of the problem. This is often referred to as [[task parallelism]].
Data-intensive is used to describe applications that are I/O bound or with a need to process large volumes of data.<ref>[https://computation.llnl.gov/casc/dcca-pub/dcca/Papers_files/data-intensive-ieee-computer-0408.pdf IEEE: Hardware Technologies for High-Performance Data-Intensive Computing], by M. Gokhale, J. Cohen, A. Yoo, and W.M. Miller, IEEE Computer, Vol. 41, No. 4, 2008, pp. 60-68.</ref> Such applications devote most of their processing time to I/O and movement and manipulation of data. [[Parallel computing|Parallel processing]] of data-intensive applications typically involves partitioning or subdividing the data into multiple segments which can be processed independently using the same executable application program in parallel on an appropriate computing platform, then reassembling the results to produce the completed output data.<ref>[http://www.agoldberg.org/Publications/DesignMethForDP.pdf IEEE: A Design Methodology for Data-Parallel Applications] {{Webarchive|url=https://web.archive.org/web/20110724225852/http://www.agoldberg.org/Publications/DesignMethForDP.pdf |date=2011-07-24 }}, by L.S. Nyland, J.F. Prins, A. Goldberg, and P.H. Mills, IEEE Transactions on Software Engineering, Vol. 26, No. 4, 2000, pp. 293-314.</ref> The greater the aggregate distribution of the data, the more benefit there is in parallel processing of the data. Data-intensive processing requirements normally scale linearly according to the size of the data and are very amenable to straightforward parallelization. The fundamental challenges for data-intensive computing are managing and processing exponentially growing data volumes, significantly reducing associated data analysis cycles to support practical, timely applications, and developing new algorithms which can scale to search and process massive amounts of data. Researchers coined the term BORPS for "billions of records per second" to measure record processing speed in a way analogous to how the term [[Million instructions per second|MIPS]] applies to describe computers' processing speed.<ref>[http://www.cse.fau.edu/~borko/HandbookofCloudComputing.html/ Handbook of Cloud Computing] {{Webarchive|url=https://web.archive.org/web/20101125065304/http://www.cse.fau.edu/~borko/HandbookofCloudComputing.html |date=2010-11-25 }}, "Data-Intensive Technologies for Cloud Computing," by A.M. Middleton. Handbook of Cloud Computing. Springer, 2010, pp. 83-86.</ref>
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== Characteristics ==
Several common characteristics of data-intensive computing systems distinguish them from other forms of computing:
# The principle of collection of the data and programs or algorithms is used to perform the computation. To achieve high performance in data-intensive computing, it is important to minimize the movement of data.<ref>[http://queue.acm.org/detail.cfm?id=1394131 Distributed Computing Economics] by J. Gray, "Distributed Computing Economics," ACM Queue, Vol. 6, No. 3, 2008, pp. 63-68.</ref> This characteristic allows processing algorithms to execute on the nodes where the data resides reducing system overhead and increasing performance.<ref
# The programming model utilized. Data-intensive computing systems utilize a machine-independent approach in which applications are expressed in terms of high-level operations on data, and the runtime system transparently controls the scheduling, execution, load balancing, communications, and movement of programs and data across the distributed computing cluster.<ref>[https://www.cs.cmu.edu/~bryant/presentations/DISC-concept.ppt Data Intensive Scalable Computing] by R.E. Bryant. "Data Intensive Scalable Computing," 2008</ref> The programming abstraction and language tools allow the processing to be expressed in terms of data flows and transformations incorporating new dataflow [[programming languages]] and shared libraries of common data manipulation algorithms such as sorting.
# A focus on reliability and availability. Large-scale systems with hundreds or thousands of processing nodes are inherently more susceptible to hardware failures, communications errors, and software bugs. Data-intensive computing systems are designed to be fault resilient. This typically includes redundant copies of all data files on disk, storage of intermediate processing results on disk, automatic detection of node or processing failures, and selective re-computation of results.
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