Section: New Results

A Framework for Enhancing Data Reuse via Associative Reordering

Participants : Kevin Stock [OSU] , Martin Kong [OSU] , Tobias Grosser [ENS Ulm] , Louis-Noël Pouchet [UCLA] , Fabrice Rastello, J. Ramanujam [LSU] , P. Sadayappan [OSU] .

The freedom to reorder computations involving associative operators has been widely recognized and exploited in designing parallel algorithms and to a more limited extent in optimizing compilers.

In this work, we develop a novel framework utilizing the associativity and commutativity of operations in regular loop computations to enhance register reuse. Stencils represent a particular class of important computations where the optimization framework can be applied to enhance performance. We show how stencil operations can be implemented to better exploit register reuse and reduce load/stores. We develop a multi-dimensional retiming formalism to characterize the space of valid implementations in conjunction with other program transformations. Experimental results demonstrate the effectiveness of the framework on a collection of high-order stencils.

This work is the fruit of the collaboration  8.1 with OSU and has been presented at the conference ACM PLDI'14.