Section: New Results

A Framework for Enhancing Data Reuse via Associative Reordering

Participants : Kevin Stock [OSU, Columbus, USA] , Louis-Noël Pouchet [UCLA, Los Angeles, USA] , Fabrice Rastello, J. Ramanujam [LSU, Houston, USA] , P. Sadayappan [OSU, Columbus, USA] .

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. However, the use of associative reordering for enhancing data locality has not been previously explored to our knowledge.

In this work, we develop a novel framework for utilizing associativity of operations in regular loop computations to enhance register reuse. Stencils represent a particular class of important computations where our optimization framework can be applied to enhance performance. We use a multi-dimensional retiming formalism to characterize the space of valid transformations and to generate the transformed code. Experimental results demonstrate the effectiveness of the framework.

This work has been submitted to PLDI'14 and is part of the collaboration with P. Sadayappan from the University of Columbus (OSU) (see Section  8.4 ).