Fine and Coarse Grain Incremental Compilation of Optimized  Code
{'Code': '05010400', 'Directorate': {'Abbreviation': 'CSE', 'LongName': 'Direct For Computer & Info Scie & Enginr'}, 'Division': {'Abbreviation': 'CCF', 'LongName': 'Division of Computing and Communication Foundations'}}
{'SignBlockName': 'Forbes D. Lewis', 'PO_EMAI': None, 'PO_PHON': None}
Incremental compilers in programming environments attempt to reduce the         recompilation effort by reusing analysis and code from a previous               compilation.  Because of the complexity, these compilers typically              limit the machine independent optimizations and register allocation             strategies to the incremental unit of granularity; a module for coarse          grain and a statement for fine grain.                                                                                                                           This project addresses the design and performance of both coarse and            fine grain incremental compiling techniques that permit optimizations           and register allocation across the incremental unit.  Techniques for            performing fine grain, coarse grain and a combination of the                    granularities will be designed, implemented and experimentally                  evaluated.  Issues such as the overhead of incremental systems, the             appropriate granularity unit and the trade-off between the amount of            information maintained and the preciseness of the recompilation                 detection process will be investigated.  The research will initially            utilize the notion of optimization and register histories previously            developed for fine grain techniques.                                                                                                                            An important aspect of this work will be the investigation into the             performance of incremental data flow techniques which are capable of            incrementally updating both intraprocedural and interprocedural                 information.  The results on the data flow investigation are also               important to compiling techniques for parallel architectures, for data          flow must be continually updated as code is transformed to increase             parallelism.
{'FirstName': 'Mary Lou', 'LastName': 'Soffa', 'PI_MID_INIT': None, 'PI_SUFX_NAME': None, 'PI_FULL_NAME': 'Mary Lou Soffa', 'EmailAddress': 'soffa@cs.virginia.edu', 'NSF_ID': '000203853', 'StartDate': '05/01/1988', 'EndDate': None, 'RoleCode': 'Principal Investigator'}
{'Name': 'University of Pittsburgh', 'CityName': 'PITTSBURGH', 'ZipCode': '152600001', 'PhoneNumber': '4126247400', 'StreetAddress': '4200 FIFTH AVENUE', 'StreetAddress2': None, 'CountryName': 'United States', 'StateName': 'Pennsylvania', 'StateCode': 'PA', 'CONGRESSDISTRICT': '18', 'CONGRESS_DISTRICT_ORG': 'PA18', 'ORG_UEI_NUM': 'MKAGLD59JRL1', 'ORG_LGL_BUS_NAME': 'UNIVERSITY OF PITTSBURGH - OF THE COMMONWEALTH SYSTEM OF HIGHER EDUCATION', 'ORG_PRNT_UEI_NUM': None}
{'Name': None, 'CityName': None, 'StateCode': None, 'ZipCode': None, 'StreetAddress': None, 'CountryCode': None, 'CountryName': None, 'StateName': None, 'CountryFlag': None, 'CONGRESSDISTRICT': None, 'CONGRESS_DISTRICT_PERF': '""'}
{'Code': '2875', 'Text': 'SYSTEM SOFTWARE'}
{'url': 'https://www.nsf.gov/awardsearch/download?DownloadFileName=1988&All=true', 'xml': '8801104.xml'}