NSF Org:	CCF Division of Computing and Communication Foundations
Recipient:	UNIVERSITY OF CALIFORNIA, LOS ANGELES
Initial Amendment Date:	August 21, 2013
Latest Amendment Date:	August 21, 2013
Award Number:	1321147
Award Instrument:	Standard Grant
Program Manager:	Anindya Banerjee abanerje@nsf.gov  (703)292-7885 CCF  Division of Computing and Communication Foundations CSE  Directorate for Computer and Information Science and Engineering
Start Date:	October 1, 2013
End Date:	April 30, 2015 (Estimated)
Total Intended Award Amount:	$424,198.00
Total Awarded Amount to Date:	$424,198.00
Funds Obligated to Date:	FY 2013 = $47,274.00
History of Investigator:	Louis-Noel Pouchet (Principal Investigator) pouchet@cs.colostate.edu Jason Cong (Co-Principal Investigator)
Recipient Sponsored Research Office:	University of California-Los Angeles 10889 WILSHIRE BLVD STE 700 LOS ANGELES CA  US  90024-4200 (310)794-0102
Sponsor Congressional District:	36
Primary Place of Performance:	University of California-Los Angeles 4732 Boelter Hall Los Angeles CA  US  90095-1596
Primary Place of Performance Congressional District:	36
Unique Entity Identifier (UEI):	RN64EPNH8JC6
Parent UEI:
NSF Program(s):	PROGRAMMING LANGUAGES
Primary Program Source:	01001314DB NSF RESEARCH & RELATED ACTIVIT
Program Reference Code(s):	7923, 7943
Program Element Code(s):	794300
Award Agency Code:	4900
Fund Agency Code:	4900
Assistance Listing Number(s):	47.070

ABSTRACT

Optimizing compilers are asked to automatically achieve good performance over an increasingly larger and heterogeneous set of architectures. Complex high-level program transformations are required to address this problem, to map the proper grain of independent computation and the proper data locality to a complex hierarchy of memory, computing and interconnection resources. The polyhedral compilation framework is one of the most powerful and flexible loop transformation system, with numerous compelling results achieved in recent years in terms of automatic program optimization (CPUs, GPUs and FPGAs). But a difficult challenge remains the deployment of those research results to larger-scale programs. Indeed, this framework uses complex mathematical algorithms that are the reason for the better program performance achieved, but which are often too time consuming for production use.

The goal of this project is to significantly improve the scalability and effectiveness of polyhedral optimizations, through the design of exact optimization methods and their associated approximation heuristics for increased scalability. We will develop novel program transformation algorithms operating under hardware resources constraints, for a variety of devices currently available on heterogeneous computing systems: for multi-core CPUs using short-vector SIMD units; for FPGAs with the help of high-level synthesis tool-chain; and for GPUs. The proposed work has the potential to significantly enhance the effectiveness of optimizing compilers thereby reducing the manual performance tuning required, with significant cost savings. The developed tools will be made publicly and freely available to the research community.

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