Difference between revisions of "Data-intensive Computing"
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==Paradigms:== | ==Paradigms:== | ||
Data-intensive computations arise in many domains of scientific and engineering research. Itself is not a driving force that would change people's view on the world, however, it does driven the development of Grid technology, because of its demanding requirement for large exchange and storage of datasets, and response time, which forces a concept of building common platform between geographically distributed processors. <br> | |||
At the same time, with the advances in the development and maturity of data-intensive computation itself, many formidable problems in areas such as physics, bioinformatics, computational astronomy, computational biology, material sciences, archeology, and oceanography may in the future be sloved, which in turn would bring new research findings and reasonably new perspectives of the world into existance. <br> | |||
==Experts:== | ==Experts:== | ||
Revision as of 21:30, 16 March 2005
Description:
In high energy physics, bioinformatics, computational astronomy, computational biology, material sciences, archeology, oceanography and many other disciplines, people encounter applications involving numerous, loosely coupled jobs that both access and generate large data sets. When the data to be accessed and processed are voluminous, we refer to the computation as data intensive. These large-scale data-intensive problems normally need to harness geographically distributed resources and greater processing power in multiple network-linked heterogeneous computer architectures exploit the best features of each for a given problem.
Enablers:
- Recent advances in database, such as data mining, data warehouse.
- Technogical advances in parallel programming
- Web services
- .NET technologies
- Software for interactive control of programs and instruments
- Scientific applications in areas such energy physics, bioinformatics, computational astronomy, computational biology, material sciences, archeology, and oceanography.
Inhibitors:
- Limitted large computational systems, data storage and specialized experimental facilities.
- Scheduling difficulty in distributed environment: i.e resource utilization, response time, global and local allocation policies.
Paradigms:
Data-intensive computations arise in many domains of scientific and engineering research. Itself is not a driving force that would change people's view on the world, however, it does driven the development of Grid technology, because of its demanding requirement for large exchange and storage of datasets, and response time, which forces a concept of building common platform between geographically distributed processors.
At the same time, with the advances in the development and maturity of data-intensive computation itself, many formidable problems in areas such as physics, bioinformatics, computational astronomy, computational biology, material sciences, archeology, and oceanography may in the future be sloved, which in turn would bring new research findings and reasonably new perspectives of the world into existance.
Experts:
United Nations US Department of Health and Human Services
Timing:
Improving on earlier methods of probabilistic forecasting, here we show that there is around an 85 per cent chance that the world's population will stop growing before the end of the century. There is a 60 per cent probability that the world's population will not exceed 10 billion people before 2100, and around a 15 per cent probability that the world's population at the end of the century will be lower than it is today. For different regions, the date and size of the peak population will vary considerably.