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GPU-Accelerated Sparse Matrix Solvers for Large-Scale Simulations

Award Information
Agency: National Aeronautics and Space Administration
Branch: N/A
Contract: NNX10CC35P
Agency Tracking Number: 094622
Amount: $99,961.00
Phase: Phase I
Program: SBIR
Solicitation Topic Code: S6.01
Solicitation Number: N/A
Timeline
Solicitation Year: 2009
Award Year: 2010
Award Start Date (Proposal Award Date): 2010-01-29
Award End Date (Contract End Date): 2010-07-29
Small Business Information
51 East Main Street, Suite 203
Newark, DE 19711-4685
United States
DUNS: 071744143
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 John Humphrey
 Principal Investigator
 (302) 456-9003
 humphrey@emphotonics.com
Business Contact
 Eric Kelmelis
Title: Business Official
Phone: (302) 456-9003
Email: kelmelis@emphotonics.com
Research Institution
N/A
Abstract

Many large-scale numerical simulations can be broken down into common mathematical routines. While the applications may differ, the need to perform functions such as matrix solves, Fourier transforms, or eigenvalue analysis routinely arise. Consequently, targeting fast, efficient implementations of these methods will benefit a large number of applications. Graphics Processing Units (GPUs) are emerging as an attractive platform to perform these types of simulations. There FLOPS/Watt and FLOPS/dollar figures are far below competing alternatives. In previous work, EM Photonics has implemented dense matrix solvers using a hybrid GPU/multicore microprocessor approach. This has shown the ability to significantly outperform either platform when used independently. In this project, we will develop a complimentary library focused on performing routines on sparse matrices. This will be extremely valuable to a wide set of users including those doing finite-element analysis and computational fluid dynamics. Using GPUs, users are able to build single workstations with an excess of four teraFLOPS of computational power as well as create large, high-performance computing systems that are efficient in terms of both cost and power. By leveraging libraries such as the ones we will develop for this project, the user is shielded from the intricacies of GPU programming while still able to access their computational performance.

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

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