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HPC Benchmark Suite NMx

Award Information
Agency: National Aeronautics and Space Administration
Branch: N/A
Contract: NNX10CB63C
Agency Tracking Number: 080065
Amount: $600,000.00
Phase: Phase II
Program: STTR
Solicitation Topic Code: T5.01
Solicitation Number: N/A
Timeline
Solicitation Year: 2008
Award Year: 2010
Award Start Date (Proposal Award Date): 2010-08-05
Award End Date (Contract End Date): 2012-08-04
Small Business Information
15400 Calhoun Drive, Suite 400
Rockville, MD 20855-2737
United States
DUNS: 161911532
HUBZone Owned: No
Woman Owned: Yes
Socially and Economically Disadvantaged: No
Principal Investigator
 Sendil Rangaswamy
 Principal Investigator
 (301) 294-4756
 sendilr@i-a-i.com
Business Contact
 Mark James
Title: Business Official
Phone: (301) 301-5221
Email: mjames@i-a-i.com
Research Institution
 University of Central Florida
 Lori Brown
 
3100 Technology Parkway
Orlando, FL 32826
United States

 (407) 882-1114
 Domestic Nonprofit Research Organization
Abstract

In the phase II effort, Intelligent Automation Inc., (IAI) and University of Central Florida (UCF) propose to develop a comprehensive numerical test suite for benchmarking current and future high performance computing activities that will include: (1) dense and unsymmetrical matrix problems faced in space aviation and problems in thermally driven structural response and radiation exchange, (2) implicit solution algorithms with production models and benchmarks for indefinite matrices and pathological cases, (3) configurations scaling for large systems in shared, distributed and mixed memory conditions, (4) documentation for strengths, weaknesses, and limitations of the toolkits used together with recommendations and (5) precision and round-off studies on serial and parallel machines, comparison of solutions on serial and parallel hardware with study of wall clock performance with respect to the number of processors
We successfully demonstrated in phase I that we can accurately and precisely benchmark run time solvers of dense complex matrices in hybrid-distributed memory architecture. We achieved highly scalable super-linear speed-up and scalability of the algorithm for large problem sizes. The tools developed in phase II will greatly improve the performance and efficiency to adapt the benchmarks to HPC systems different hardware architectures at NASA facilities and for non-NASA commercial applications.

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

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