Parallel‐processing applications for data analysis in the social sciences

Abstract

AbstractWe examine parallel‐processing applications to the analysis of large data sets typically used in social science research. Our research uses a parallel environment which makes it possible to have 1024 processors working simultaneously on a problem. The application is tested using various configurations of number of processors and block‐size of data reads on the estimation of a linear model of earnings for the California portion of the 15% sample of the 1970 Census. Performance factors assessed include total execution time, speed‐up and efficiency. Execution times are also compared with reference to execution times on an IBM 3081 using SPSS‐X. Results indicate that optimal configurations of number of processors and data block‐size can produce significantly faster execution times for linear model estimation on relatively large (80,000 cases) data sets. We also discuss other applications of parallel processing to statistical analyses commonly found in social science.