Index to NASA Tech Briefs, 1975

This index contains abstracts and four indexes--subject, personal author, originating Center, and Tech Brief number--for 1975 Tech Briefs

Center for Programming Models for Scalable Parallel Computing - Towards Enhancing OpenMP for Manycore and Heterogeneous Nodes

OpenMP was not well recognized at the beginning of the project, around year 2003, because of its limited use in DoE production applications and the inmature hardware support for an efficient implementation. Yet in the recent years, it has been graduately adopted both in HPC applications, mostly in the form of MPI+OpenMP hybrid code, and in mid-scale desktop applications for scientific and experimental studies. We have observed this trend and worked deligiently to improve our OpenMP compiler and runtimes, as well as to work with the OpenMP standard organization to make sure OpenMP are evolved in the direction close to DoE missions. In the Center for Programming Models for Scalable Parallel Computing project, the HPCTools team at the University of Houston (UH), directed by Dr. Barbara Chapman, has been working with project partners, external collaborators and hardware vendors to increase the scalability and applicability of OpenMP for multi-core (and future manycore) platforms and for distributed memory systems by exploring different programming models, language extensions, compiler optimizations, as well as runtime library support

A Spatial Analysis of Crime for the City of Omaha

The spatial patterns of four types of crimes (assault, robbery, auto-theft, and burglary) and their relationships with the selected socio-economic characteristics for the City of Omaha, Nebraska, were examined in this research. The crime data were based on the 2000 police reported crime and the socio-economic data were extracted from the 1997 American Community Survey and land use data from the 2000 Omaha parcel file. The location quotients of crimes (LQCs) were used to measure the relative specialization and structure of crimes for each census tract, and as the dependent variables for the statistical analysis. GIS techniques such as geocoding, spatial aggregation, and spatial analysis were used for crime mapping and crime analysis. Factor analysis and multiple regression models were employed to reveal the crime-causation relationships. Major findings of this research include: (1) LQCs highlight the specialization of crime and can be effectively used for GIS-based visualization and statistical analysis of crime; (2) the North Omaha and the downtown areas (high-crime districts) have relatively higher occurrences of violent crime and diversified structure of crimes while west Omaha (low crime districts) has a relatively specialized crime structure that is dominated by property crimes; (3) a modest proportion of the variance of crimes can be significantly explained by the statistical models