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TOWARDS EXASCALE DISTRIBUTED DATA MANAGEMENT
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DOI：10.1177/1094342009347702.png)
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Abstract
En 中文
Exascale eScience infrastructures will face important and critical challenges, both from computational and data perspectives. Increasingly complex and parallel scientific codes will lead to the production of a huge amount of data. The large volume of data and the time needed to locate, access, analyze and visualize data will greatly impact on the scientific productivity of scientists and researchers in several domains. Significant improvements in the data management field will increase research productivity in solving complex scientific problems. Next-generation eScience infrastructures will start from the assumption that exascale high-performance computing (HPC) applications (running on million of cores) will generate data at a very high rate (terabytes/s). Hundreds of exabytes of data (distributed across several centers) are expected, by 2020, to be available through heterogeneous storage resources for access, analysis, post-processing and other scientific activities.
Keywords:
distributed data management
data replication
metadata management
data analysis
parallel I/O
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