  Consider a big data multiple testing task, where, due to storage and
computational bottlenecks, one is given a very large collection of p-values by
splitting into manageable chunks and distributing over thousands of computer
nodes. This paper is concerned with the following question: How can we find the
full data multiple testing solution by operating completely independently on
individual machines in parallel, without any data exchange between nodes? This
version of the problem tends naturally to arise in a wide range of
data-intensive science and industry applications whose methodological solution
has not appeared in the literature to date; therefore, we feel it is necessary
to undertake such analysis. Based on the nonparametric functional statistical
viewpoint of large-scale inference, started in Mukhopadhyay (2016), this paper
furnishes a new computing model that brings unexpected simplicity to the design
of the algorithm which might otherwise seem daunting using classical approach
and notations.
