SBIR Phase I:GPU Software for Sparse Linear Programming

Award Information
Agency: National Science Foundation
Branch: N/A
Contract: 1014271
Agency Tracking Number: 1014271
Amount: $150,000.00
Phase: Phase I
Program: SBIR
Awards Year: 2010
Solicitation Year: 2010
Solicitation Topic Code: IC
Solicitation Number: NSF 09-609
Small Business Information
The Optimization Firm, LLC
932 Waterview Way, Apartment D, Champaign, IL, 61822
DUNS: 832759141
HUBZone Owned: N
Woman Owned: N
Socially and Economically Disadvantaged: N
Principal Investigator
 Joseph Elble
 PhD
 (217) 418-9239
 joseph.elble@gmail.com
Business Contact
 Joseph Elble
Title: PhD
Phone: (217) 418-9239
Email: joseph.elble@gmail.com
Research Institution
N/A
Abstract
This Small Business Innovation Research (SBIR) Phase I project will investigate the feasibility of developing a practical parallel implementation of the simplex algorithm for linear programming. As the demand to solve larger and more complex decision-making problems in all sectors of the economy increases, so does the demand for a parallel simplex algorithm for on-line and time-critical operations. Yet, parallel versions of this algorithm are currently unable to outperform appropriate serial codes for industrially-relevant problems that involve sparse large-scale matrices. The proposed research will explore a finer-grain parallelization than previously attempted for the simplex algorithm. As a result, the proposed implementation of the simplex algorithm will harness recent advances in graphics processing units (GPUs), while being capable of benefiting from any future serial algorithmic advances in the context of this algorithm. In addition to callable libraries and stand-alone executables, the implementation will be offered through cloud-enabled commercial services. The parallelization of the simplex algorithm has remained a major challenge in scientific computing. The proposed research will involve a systematic investigation of different combinations of factorization and pricing algorithms with regard to their performance on modern GPUs. If successful, the proposed work will result in an enabling technology for decision making in diverse areas across science and engineering and commerce. Linear programming is used extensively in applications ranging from data mining to airline scheduling, cancer therapy, engineering design, financial decision making, and logistics. As a result, the proposed work presents an unique opportunity to improve the competitiveness of the nation's commercial infrastructure.

* information listed above is at the time of submission.

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