Difference between revisions of "Data-intensive Computing"
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In high energy physics, bioinformatics, and other disciplines, we encounter applications involving numerous, loosely coupled jobs that both access and generate large data sets, these are large-scale data-intensive problems that normally need to harness geographically distributed resources. | |||
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==Enablers:== | ==Enablers:== | ||
Revision as of 17:53, 16 March 2005
Description:
In high energy physics, bioinformatics, and other disciplines, we encounter applications involving numerous, loosely coupled jobs that both access and generate large data sets, these are large-scale data-intensive problems that normally need to harness geographically distributed resources.
Enablers:
- Technogical adavnces in GRID
- Technogical advances in parallel programming
- Increasing demand for large computational systems, data storage and specialized experimental facilities.
- Collaborative engineering
- Need for browsing of remote datasets
- Need for Usage of remote software
- Large-scale parameter studies
Inhibitors:
- Extending the retirement age to another 10 years so people will have to work more
Paradigms:
There has been enormous concern about the consequences of human population growth for the environment and for social and economic development. But this growth is likely to come to an end in the foreseeable future.
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.