  Recently popularized randomized methods for principal component analysis
(PCA) efficiently and reliably produce nearly optimal accuracy --- even on
parallel processors --- unlike the classical (deterministic) alternatives. We
adapt one of these randomized methods for use with data sets that are too large
to be stored in random-access memory (RAM). (The traditional terminology is
that our procedure works efficiently "out-of-core.") We illustrate the
performance of the algorithm via several numerical examples. For example, we
report on the PCA of a data set stored on disk that is so large that less than
a hundredth of it can fit in our computer's RAM.
