Parallel and Vector Computing for Nonlinear Network         Optimization
{'Code': '05010200', 'Directorate': {'LongName': 'Direct For Computer & Info Scie & Enginr'}, 'Division': {'LongName': 'Division of Computing and Communication Foundations'}}
{'SignBlockName': 'S. Kamal Abdali'}
This proposal is to examine parallel computing for network optimization         problem from three different directions: small scale parallelism and            vector supercomputing, medium scale parallelism, and massively parallel         computing.  A primal truncated Newton algorithm will be specialized for         the environment of vector and multiprocessor supercomputers.  Novel             algorithms based on speculative decomposition will be implemented on            medium scale parallel computers.  The suitability of massively parallel         computers - - with thousands of processing elements - - will be                 investigated using distributed relaxation algorithms.  A comparison             across algorithms and machines and with currently used procedures will          be used with real data.  The final goal is to solve ultra-large                 problems having about 10,000 nodes and 1,000,000 arcs.
{'FirstName': 'Stavros', 'LastName': 'Zenios', 'EmailAddress': 'ZENIOS@Wharton.upenn.edu', 'StartDate': '12/15/1988', 'EndDate': None, 'RoleCode': 'Principal Investigator'}
{'Name': 'University of Pennsylvania', 'CityName': 'Philadelphia', 'ZipCode': '191046205', 'PhoneNumber': '2158987293', 'StreetAddress': 'Research Services', 'CountryName': 'United States', 'StateName': 'Pennsylvania', 'StateCode': 'PA'}
{'Code': '2865', 'Text': 'NUMERIC, SYMBOLIC & GEO COMPUT'}
{'url': 'https://www.nsf.gov/awardsearch/download?DownloadFileName=1988&All=true', 'xml': '8811135.xml'}