Data Centric Computing for Nuclear Physics

Award Information
Agency: Department of Energy
Branch: N/A
Contract: DE-FG02-11ER90138
Agency Tracking Number: 97777
Amount: $150,000.00
Phase: Phase I
Program: SBIR
Awards Year: 2011
Solicitation Year: 2011
Solicitation Topic Code: 42 c
Solicitation Number: DE-FOA-0000413
Small Business Information
Virkaz Technologies Llc
865 Ashfield Drive, Decatur, GA, 30030-5314
DUNS: 827429494
HUBZone Owned: N
Woman Owned: N
Socially and Economically Disadvantaged: Y
Principal Investigator
 Charles Earl
 Dr.
 (404) 932-1645
 Charlescearl@virkaz.com
Business Contact
 Charles Earl
Title: Dr.
Phone: (404) 932-1645
Email: Charlescearl@virkaz.com
Research Institution
 Stub
Abstract
The infrastructure developed by Google for its MapReduce algorithm has obviously had an impact upon the world of search. There has been an emergence of algorithmic implementations of the MapReduce framework to support computing in the bio-sciences, machine learning, and general high performance computing. The basic attractiveness of MapReduce and other data-oriented topologies is that they provide a framework for data-centric computation: parallel computations are mapped across fixed data stores thus reaping the benefits of locality for situations where the cost of data transfer to a compute node would far outweigh the cost of transferring computation to the data store. The ultimate benefit of this paradigm therefore is that researchers could define virtual machines that contain standard analysis images, migrate those to the data, and then run MapReduce on datasets for which this analysis makes sense. The ultimate goal of this project (phase I and II) is to develop libraries and adaptation modules that will allow virtualized Nuclear Physics software to run efficiently under MapReduce

* information listed above is at the time of submission.

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