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Award Abstract #1229282

MRI: Development of a GPU-Enabled, Petascale Active Storage Architecture for Data-Intensive Applications in HPC and Cloud Environments

Division Of Computer and Network Systems
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Initial Amendment Date: August 21, 2012
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Latest Amendment Date: September 29, 2014
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Award Number: 1229282
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Award Instrument: Standard Grant
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Program Manager: Rita V. Rodriguez
CNS Division Of Computer and Network Systems
CSE Direct For Computer & Info Scie & Enginr
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Start Date: October 1, 2012
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End Date: September 30, 2016 (Estimated)
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Awarded Amount to Date: $300,000.00
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Investigator(s): Purushotham Bangalore puri@cis.uab.edu (Principal Investigator)
Anthony Skjellum (Former Principal Investigator)
Anthony Skjellum (Co-Principal Investigator)
Chengcui Zhang (Co-Principal Investigator)
Ragib Hasan (Co-Principal Investigator)
Purushotham Bangalore (Former Co-Principal Investigator)
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Sponsor: University of Alabama at Birmingham
AB 1170
Birmingham, AL 35294-0001 (205)934-5266
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Program Reference Code(s): 1189, 9150
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Program Element Code(s): 1640


Proposal #: 12-29282

PI(s): Skjellum, Anthony

Bangalore, Purushotham; Hasan, Ragib; Zhang, Chengcui

Institution: University of Alabama at Birmingham

Title: MRI/Dev.: A GPU-Enabled, Petascale Active Storage Architecture for Data-Intensive Applications in HPC and Cloud Environments

Project Proposed:

This project, developing a 2.4 Petabytes (PB) of raw storage instrument to support a variety of research projects in experimental HPC and cloud storage, aims to both increase local resources for scientific computing and act as a testbed for GPU-enabled reliable storage. The instrument enables an increased virtualization of storage, the concurrent access to storage under fault scenarios (e.g., RAID), and a series of data intensive applications. Lessons learned will be leveraged from the existing system in place, whereby the existing system and the new system will be integrated in a way that supports cloud and disaster recovery modes of operation. The project enables the following studies and research projects:

- Studying of effective rates of errors and reliability at highly refined levels and seeking means to identify and manage additional classes of errors (e.g., misdirected writes);

- Creating semi-analytical models to allow tunable storage characteristics within a lifetime-reliability-performance cost space;

- Running applications from data mining (including bioinformatics as drivers for proving the efficacy of the final system), to achieve new science in these data-intensive domains; and

- Conducting computer science research aimed at simplifying use of active storage computation.

Broader Impacts:

This instrumentation increases the institution?s capacity to conduct cutting-edge research in an inexpensive, fast, practical, reliable petascale storage for data-intensive applications. Significant computational power logically close to that storage enables new science. Student training (including underrepresented groups) will be emphasized. The knowledge dissemination through this effort could be significant.


Please report errors in award information by writing to: awardsearch@nsf.gov.



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