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Indexing large scientific data

Award Information
Agency: Department of Defense
Branch: Defense Advanced Research Projects Agency
Contract: W911QX-13-C-0031
Agency Tracking Number: D123-002-0048
Amount: $148,729.00
Phase: Phase I
Program: SBIR
Solicitation Topic Code: SB123-002
Solicitation Number: 2012.3
Timeline
Solicitation Year: 2012
Award Year: 2013
Award Start Date (Proposal Award Date): 2013-02-04
Award End Date (Contract End Date): 2013-11-04
Small Business Information
PO BOX 8482
Atlanta, GA -
United States
DUNS: 962534223
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Glenn Kirbo
 Chief Technology Officer
 (404) 217-0457
 glenn@maplarge.com
Business Contact
 Lynwood Bishop
Title: President
Phone: (404) 217-0457
Email: lynwood@maplarge.com
Research Institution
N/A
Abstract

Hadoop style systems have done an excellent job of providing scalable long term disk bound data storage and enjoy wide acceptance in both Government and the private sector. However, Hadoop implementations suffer from performance limitations with respect to whole set aggregates and real time interactivity that we believe can be solved by optimizing for local memory operations. The key performance driver is memory locality. A well written Hadoop process might sometimes achieve optimal memory throughput on an individual node, but the overall system does not generally result in optimal memory locality and thus frequently fails performance requirements. We propose to create a multi node data architecture that automatically optimizes for memory locality using a compressed column oriented architecture compatible with both CPU and GPU processing. The result will be a real time streaming architecture capable of indexing and querying large volumes of heterogeneous scientific data stored on clusters of cloud computers.

* Information listed above is at the time of submission. *

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