  We address a common problem in large-scale data analysis, and especially the
field of genetics, the huge-scale testing problem, where millions to billions
of hypotheses are tested together creating a computational challenge to perform
multiple hypotheses testing procedures. As a solution we propose an alternative
algorithm to the well used Linear Step Up procedure of Benjamini and Hochberg
(1995). Our algorithm requires linear time and does not require any p-value
ordering. It permits separating huge-scale testing problems arbitrarily into
computationally feasible sets or chunks. Results from the chunks are combined
by our algorithm to produce the same results as the controlling procedure on
the entire set of tests, thus controlling the global false discovery rate even
when p-values are arbitrarily divided. The practical memory usage may also be
determined arbitrarily by the size of available memory.
