NASA SBIR 2011 Solicitation

FORM B - PROPOSAL SUMMARY


PROPOSAL NUMBER: 11-1 S6.01-9256
SUBTOPIC TITLE: Technologies for Large-Scale Numerical Simulation
PROPOSAL TITLE: High-Quality Random Number Generation Software for High-Performance Computing

SMALL BUSINESS CONCERN (Firm Name, Mail Address, City/State/Zip, Phone)
DANIEL H. WAGNER ASSOCIATES, INC.
559 West Uwchlan Avenue, Suite 140
Exton, PA 19341 - 3013
(757) 727-7700

PRINCIPAL INVESTIGATOR/PROJECT MANAGER (Name, E-mail, Mail Address, City/State/Zip, Phone)
Timothy D Andersen
tim@va.wagner.com
2 Eaton St., Suite 500
Hampton, VA 23669 - 4045
(757) 727-7700

Estimated Technology Readiness Level (TRL) at beginning and end of contract:
Begin: 1
End: 3

TECHNICAL ABSTRACT (Limit 2000 characters, approximately 200 words)
Random number (RN) generation is the key software component that permits random sampling. Software for parallel RN generation (RNG) should be based on RNGs that are good serial generators, but also are suitable for concurrent execution, i.e. RNs produced by different concurrent processes must be statistically independent of one another. With dependence, the work done on one concurrent process will be partially redone on another process, thus reducing the efficiency of the concurrent computation and defeating the purpose of concurrency. Although parallel RNGs exist for older parallel computing paradigms such as clusters, modern advances in computer architecture, specifically the hardware support for multithreading via multicore and other architectures, and widely available computational coprocessor technologies, such as the General Purpose Graphics Processing Unit (GPGPU) have created the need for high-quality RNG software to support these new architectural features. Thus, this Phase I research project addresses these new architectures from a mathematically rigorous stand-point of preventing statistical dependence and allowing for reproducibility in modeling and simulation as well as other applications. In addition, it investigates applications to small memory technologies such as embedded systems and radio-frequency identification (RFID) chips.

POTENTIAL NASA COMMERCIAL APPLICATIONS (Limit 1500 characters, approximately 150 words)
The main beneficiary of the proposed tool is NASA Ames Research Center's Advanced Supercomputing Division (http://www.nas.nasa.gov/) which provides tools and resources for supercomputing at NASA. Random number generation is important in any project with a stochastic component and the advantage of a GPGPU approach is a major saving in money for computational power. A number of other NASA centers rely on detailed simulations of stochastic processes on supercomputers. The NASA Center for Climate Simulation (http://www.nccs.nasa.gov/) provides climate simulation tools and high-performance computing resources for NASA projects. Such simulations would benefit from GPGPU random number generation tools. NASA is a key participant in the federal High Performance Computing and Communications (HPCC) Program (http://www.hq.nasa.gov/hpcc/mission.htm) to extend U.S. technological leadership in high-performance computing and communications for the benefit of NASA stakeholders: the U.S. aeronautics, Earth and space sciences, and spaceborne research communities. The projects NASA HPCC supports would also benefit from the proposed tools.

POTENTIAL NON-NASA COMMERCIAL APPLICATIONS (Limit 1500 characters, approximately 150 words)
The market for parallel random number generators is large and expanding. Virtually all scientific computing applications have a stochastic component requiring random numbers and any parallelization requires a parallel RNG to avoid coherence. The three largest RNG-consuming applications are (1) discrete-event simulation including war gaming, many systems-level simulation tools, and video games (e.g., Army training simulations); (2) particle Monte Carlo for simulating nuclear weapons and other large nuclear processes (astrophysics) and for planning treatments for radiology; and (3) finance where MC based computations are mandated in many nations under the Basel II accord for banking supervision; insurance companies, hedge funds, and investment firms rely on MC to price derivative products. Other consumers are intelligence agencies doing cryptanalysis and cryptography, aerospace and defense contractors doing modeling and simulation, and universities and federally funded research labs/national labs that do noise modeling and statistical analysis.

TECHNOLOGY TAXONOMY MAPPING (NASA's technology taxonomy has been developed by the SBIR-STTR program to disseminate awareness of proposed and awarded R/R&D in the agency. It is a listing of over 100 technologies, sorted into broad categories, of interest to NASA.)
Computer System Architectures
Data Modeling (see also Testing & Evaluation)
Data Processing


Form Generated on 11-22-11 13:43