The main features of the HPPRINCOMP procedure are as follows:
can perform analysis on a massively parallel SAS high-performance appliance
reads input data in parallel and writes output data in parallel when the data source is the appliance database
is highly multithreaded during calculations of the sum-of-squares-and-crossproducts (SSCP) matrix and the principal component scores
supports a PARTIAL statement for analyzing a partial correlation or covariance matrix
supports a FREQ statement for grouped analysis
supports a WEIGHT statement for weighted analysis
produces an output data set that contains principal component scores
produces an output data set that contains means, standard deviations, number of observations, correlations or covariances, eigenvalues, and eigenvectors