| 翻訳と辞書 |
| Data-intensive computing ： ウィキペディア英語版 |
Data-intensive computing
Data-intensive computing is a class of parallel computing applications which use a data parallel approach to processing large volumes of data typically terabytes or petabytes in size and typically referred to as big data. Computing applications which devote most of their execution time to computational requirements are deemed compute-intensive, whereas computing applications which require large volumes of data and devote most of their processing time to I/O and manipulation of data are deemed data-intensive.〔(Handbook of Cloud Computing ), "Data-Intensive Technologies for Cloud Computing," by A.M. Middleton. Handbook of Cloud Computing. Springer, 2010.〕
== Introduction ==
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 needs 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”.〔( An Information Avalanche ), by Vinton Cerf, IEEE Computer, Vol. 40, No. 1, 2007, pp. 104-105.〕 An 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.〔( The Expanding Digital Universe ), by J.F. Gantz, D. Reinsel, C. Chute, W. Schlichting, J. McArthur, S. Minton, J. Xheneti, A. Toncheva, and A. Manfrediz, IDC, White Paper, 2007.〕 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.〔( How Much Information? 2003 ), by P. Lyman, and H.R. Varian, University of California at Berkeley, Research Report, 2003.〕 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.〔( Got Data? A Guide to Data Preservation in the Information Age ), by F. Berman, Communications of the ACM, Vol. 51, No. 12, 2008, pp. 50-56.〕 Data-intensive computing is intended to address this need.
Parallel processing approaches can be generally classified as either ''compute-intensive'', or ''data-intensive''.〔( Models and languages for parallel computation ), by D.B. Skillicorn, and D. Talia, ACM Computing Surveys, Vol. 30, No. 2, 1998, pp. 123-169.〕〔(Computing in the 21st Century ), by I. Gorton, P. Greenfield, A. Szalay, and R. Williams, IEEE Computer, Vol. 41, No. 4, 2008, pp. 30-32.〕〔( High-Speed, Wide Area, Data Intensive Computing: A Ten Year Retrospective ), by W.E. Johnston, IEEE Computer Society, 1998.〕 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.〔( 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.〕 Such applications devote most of their processing time to I/O and movement and manipulation of data. 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.〔( IEEE: A Design Methodology for Data-Parallel Applications ), 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.〕 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 MIPS applies to describe computers' processing speed.〔(Handbook of Cloud Computing ), "Data-Intensive Technologies for Cloud Computing," by A.M. Middleton. Handbook of Cloud Computing. Springer, 2010, pp. 83-86.〕
抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』
■ウィキペディアで「Data-intensive computing」の詳細全文を読む
スポンサード リンク
| 翻訳と辞書 : 翻訳のためのインターネットリソース |
Copyright(C) kotoba.ne.jp 1997-2016. All Rights Reserved.
|
|