Saturday, January 1, 2011

Research ICT: Exascale I/O for scientific computing

I would be interested to hear if there are companies doing fundamental changes in the I/O stack for data analytics efficiency. See research discussion below:

Bringing Exascale I/O Within Science's Reach: Middleware for Enabling and Simplifying Scientific Access to Extreme Scale Parallel I/O Infrastructure

Research Area: Extreme Scale Scientific Data Management and Analysis
Principal Investigator: Prabhat of Berkeley Lab
Contributors: Wes Bethel and John Wu of Berkeley Lab

Computer scientists widely agree that data size and complexity impede modern computational and experimental science. This project will address these challenges by building upon existing data model application programming interfaces (APIs) that simplify simulation and analysis code development by encapsulating the complexity of parallel I/O (input/output); incorporate advanced index/query technology to accelerate operations that are common to scientific data analysis; and extend the scalability of I/O middleware to make effective use of current and future computational platforms. The project will work closely with specific DOE science code teams to ensure that the new capabilities are responsive to scientists' needs and are usable in production environments.

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