NSF Org:	CCF Division of Computing and Communication Foundations
Recipient:	CORNELL UNIVERSITY
Initial Amendment Date:	August 29, 2018
Latest Amendment Date:	December 13, 2018
Award Number:	1822805
Award Instrument:	Standard Grant
Program Manager:	Damian Dechev CCF  Division of Computing and Communication Foundations CSE  Directorate for Computer and Information Science and Engineering
Start Date:	October 1, 2018
End Date:	September 30, 2019 (Estimated)
Total Intended Award Amount:	$68,333.00
Total Awarded Amount to Date:	$0.00
Funds Obligated to Date:	FY 2018 = $0.00
History of Investigator:	Elaine Shi (Principal Investigator) runting@cs.cmu.edu
Recipient Sponsored Research Office:	Cornell University 341 PINE TREE RD ITHACA NY  US  14850-2820 (607)255-5014
Sponsor Congressional District:	19
Primary Place of Performance:	Cornell University 107 Hoy Road Ithaca NY  US  14853-7501
Primary Place of Performance Congressional District:	19
Unique Entity Identifier (UEI):	G56PUALJ3KT5
Parent UEI:
NSF Program(s):	PPoSS-PP of Scalable Systems
Primary Program Source:	01001819DB NSF RESEARCH & RELATED ACTIVIT
Program Reference Code(s):	026Z
Program Element Code(s):	042Y00
Award Agency Code:	4900
Fund Agency Code:	4900
Assistance Listing Number(s):	47.070

ABSTRACT

Modern computing systems have moved beyond single-core, single-processor devices to more modern multi-core parallel processors operating in networked systems and available in warehouse-scale clouds popularized by industries and the government. This new parallel, interconnected, big-data world requires fundamental research on multiple levels from algorithms to systems and computer architecture. This project seeks to take initial steps in the study of the expansive set of algorithms and systems issues in this important research challenge by building and developing new general frameworks for massive parallel computation, often involving privacy and security, in real-life scenarios.

The investigators' long-term goals include two directions. As the first thrust of this effort, the investigators aim to design fundamental and efficient algorithms for massive parallel computations in the practical MapReduce framework, in particular by reducing the number of rounds in this framework. As the second thrust of this effort, the investigators aim to augment current parallel environments and architectures with better data structures and abstractions to develop simplified and fast implementations of fundamental algorithms such that everyone can use them in practice.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

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