On Simplifying Dependent Polyhedral Reductions

Abstract: \emph{Reductions} combine collections of input values with an associative (and usually also commutative) operator to produce either a single, or a collection of outputs. They are ubiquitous in computing, especially with big data and deep learning. When the \emph{same} input value contributes to multiple output values, there is a tremendous opportunity for reducing (pun intended) the computational effort. This is called \emph{simplification}. \emph{Polyhedral reductions} are reductions where the input and output data collections are (dense) multidimensional arrays (i.e., \emph{tensors}), accessed with linear/affine functions of the indices. % \emph{generalized tensor contractions} Gautam and Rajopadhye \cite{sanjay-popl06} showed how polyhedral reductions could be simplified automatically (through compile time analysis) and optimally (the resulting program had minimum asymptotic complexity). Yang, Atkinson and Carbin \cite{yang2020simplifying} extended this to the case when (some) input values depend on (some) outputs. Specifically, they showed how the optimal simplification problem could be formulated as a bilinear programming problem, and for the case when the reduction operator admits an inverse, they gave a heuristic solution that retained optimality. In this note, we show that simplification of dependent reductions can be formulated as a simple extension of the Gautam-Rajopadhye backtracking search algorithm.