
NSF Org: |
CCF Division of Computing and Communication Foundations |
Recipient: |
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Initial Amendment Date: | August 10, 2009 |
Latest Amendment Date: | July 17, 2014 |
Award Number: | 0905509 |
Award Instrument: | Standard Grant |
Program Manager: |
Sankar Basu
sabasu@nsf.gov (703)292-7843 CCF Division of Computing and Communication Foundations CSE Directorate for Computer and Information Science and Engineering |
Start Date: | September 1, 2009 |
End Date: | August 31, 2015 (Estimated) |
Total Intended Award Amount: | $800,000.00 |
Total Awarded Amount to Date: | $800,000.00 |
Funds Obligated to Date: |
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History of Investigator: |
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Recipient Sponsored Research Office: |
200 UNIVERSTY OFC BUILDING RIVERSIDE CA US 92521-0001 (951)827-5535 |
Sponsor Congressional District: |
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Primary Place of Performance: |
200 UNIVERSTY OFC BUILDING RIVERSIDE CA US 92521-0001 |
Primary Place of
Performance Congressional District: |
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Unique Entity Identifier (UEI): |
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Parent UEI: |
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NSF Program(s): |
DES AUTO FOR MICRO & NANO SYST, COMPILERS, COMPUTER ARCHITECTURE |
Primary Program Source: |
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Program Reference Code(s): |
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Program Element Code(s): |
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Award Agency Code: | 4900 |
Fund Agency Code: | 4900 |
Assistance Listing Number(s): | 47.070 |
ABSTRACT
Hybrid multiprocessor architectures present an unprecedented opportunity for high performance computing through the seamless integration of large number of processors and hardware accelerators. This project addresses the research challenges in the design and exploitation of hybrid multiprocessors through innovations that span across the areas of architectures, compilers, and high-performance computing. A hybrid cache coherent non-uniform memory access (CC-NUMA) architecture is designed that clusters CPUs, hardware accelerators, and memories to preserve locality and reduce memory latency. Partitioning models are developed to enable optimal partitioning of data among CPUs and hardware accelerators. Compiler techniques are developed for detection of parallelism, its partitioning, and assignment across CPUs and hardware accelerators. The project enables coexistence of data streaming (push data) and data fetching (pull data) mechanisms. The research benefits from detailed measurements using a 64-processor SGI Altix 4700 CC-NUMA machine with FPGAs, the Intel FSB-FPGA architecture accelerator, and Niveus 4000 workstation with NVIDIA GPUs.
The research has impact on large-scale scientific computing. The hybrid multiprocessor technology is likely to be transferred to industry while the developed software (compilers, simulators and Hybrid SPLASH-2 benchmarks) will be distributed to researchers. The project also has impact on education and research. The SGI Altix machine is already being used in our graduate classes and further projects on hybrid parallel computing are introduced in architecture, parallel processing, and compiler classes. The project contributes to minority undergraduate education in Computer Science since UCR is recognized for its large undergraduate Hispanic population.
PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH
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