NSF Org:	CCF Division of Computing and Communication Foundations
Recipient:	NEW MEXICO STATE UNIVERSITY
Initial Amendment Date:	August 10, 2011
Latest Amendment Date:	September 22, 2015
Award Number:	1111798
Award Instrument:	Continuing Grant
Program Manager:	Almadena Chtchelkanova achtchel@nsf.gov  (703)292-7498 CCF  Division of Computing and Communication Foundations CSE  Directorate for Computer and Information Science and Engineering
Start Date:	August 1, 2011
End Date:	July 31, 2016 (Estimated)
Total Intended Award Amount:	$899,906.00
Total Awarded Amount to Date:	$899,906.00
Funds Obligated to Date:	FY 2011 = $675,653.00 FY 2014 = $224,253.00
History of Investigator:	Jeanine Cook (Principal Investigator) jcook@nmsu.edu
Recipient Sponsored Research Office:	New Mexico State University 1050 STEWART ST. LAS CRUCES NM  US  88003 (575)646-1590
Sponsor Congressional District:	02
Primary Place of Performance:	New Mexico State University 1050 STEWART ST. LAS CRUCES NM  US  88003
Primary Place of Performance Congressional District:	02
Unique Entity Identifier (UEI):	J3M5GZAT8N85
Parent UEI:
NSF Program(s):	Software & Hardware Foundation, EPSCoR Co-Funding
Primary Program Source:	01001112DB NSF RESEARCH & RELATED ACTIVIT 01001415DB NSF RESEARCH & RELATED ACTIVIT
Program Reference Code(s):	7925, 7942, 9150
Program Element Code(s):	779800, 915000
Award Agency Code:	4900
Fund Agency Code:	4900
Assistance Listing Number(s):	47.070

ABSTRACT

The most powerful computing systems in the world have historically been dedicated to solving scientific problems. Until recently, the computations performed by these systems have typically been simulations of various physical phenomena. However, a new paradigm for scientific discovery has been steadily rising in importance, namely, data-intensive science, which focuses sophisticated analysis techniques on the enormous (and ever increasing) amounts of data being produced in scientific, commercial, and social endeavors. Important research based on data-intensive science include areas as diverse as knowledge discovery, bioinformatics, proteomics and genomics, data mining and search, electronic design automation, computer vision, and Internet routing. Unfortunately, the computational approaches needed for data-intensive science differ markedly from those that have been so effective for simulation-based supercomputing. To enable and facilitate efficient execution of data-intensive scientific problems, this project will develop a comprehensive hardware and software supercomputing system for data-intensive science.
Graph algorithms and data structures are fundamental to data-intensive computations and, consequently, this project is focused on providing fundamental, new understandings of the basics of large-scale graph processing and how to build scalable systems to efficiently solve large-scale graph problems. In particular, this work will characterize processing overheads and the limits of graph processing scalability, develop performance models that properly capture graph algorithms, define the (co-design) process for developing graph-specific hardware, and experimentally verify our approach with a prototype execution environment. Key capabilities of our system include: a novel fine-grained parallel programming model, a scalable library of graph algorithms and data structures, graph-optimized core architecture, and a scalable graph execution platform. The project will also address the programming challenges involved in constructing scalable and reliable software for data-intensive problems.

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