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Innovative CFD Algorithm, Libraries & Python Frameworks for Hybrid-GPU Computing Architectures

Award Information
Agency: Department of Defense
Branch: Air Force
Contract: FA9550-10-C-0149
Agency Tracking Number: F09B-T18-0126
Amount: $99,877.00
Phase: Phase I
Program: STTR
Solicitation Topic Code: AF09-BT18
Solicitation Number: 2009.B
Solicitation Year: 2009
Award Year: 2010
Award Start Date (Proposal Award Date): 2010-06-16
Award End Date (Contract End Date): 2011-03-16
Small Business Information
301 Route 17 N 7th Floor
Rutherford, NJ 07070
United States
DUNS: 927751149
HUBZone Owned: No
Woman Owned: Yes
Socially and Economically Disadvantaged: No
Principal Investigator
 Earl P.N. Duque
 Manager of Applied Research
 (928) 600-2450
Business Contact
 Steve Legensky
Title: General Manager
Phone: (201) 460-4700
Research Institution
 Univeristy of California, Davis
 Bernadine Smith
Office of Research, Sponsored 1850 Research Park Drive
Davis, CA 95618
United States

 (530) 754-7958
 Nonprofit College or University

The need for faster highly resolved solutions coupled with the advent of General Purpose Graphics Processing Unit (GPGPU) architectures and the development of GPGPU algorithms at the University of California, Davis present an opportunity that JMSI Inc. proposes to leverage by developing algorithmic and software solutions for GPGPUs in “Innovative CFD Algorithms, Libraries & Python Frameworks for Hybrid CPU-GPU Compute Architectures”. In the Phase 1 effort proposed herein, JMSI Inc. proposes to develop prototype algorithms for the core CFD primitives and matrix solvers that can be used with any CFD code through either libraries or a Python Interface Framework. The prototypes are based upon GPGPU research produced by the University COLLABORATORs, Profs. John Owens and Roger Davis, from the University of California, Davis. The MBFLO code by Prof. Davis will be used to demonstrate the core CFD primitives while a development implicit code will be used to demonstrate the use of implicit time stepping algorithms by Owens that are amenable to GPGPU architectures. BENEFIT: Accelerated CFD solutions at lower cost. Open source GPGPU libraries and Python Components that can be used with any CFD solver.

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

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