Section: New Results

Beyond Reuse Distance Analysis: Dynamic Analysis for Characterization of Data Locality Potential

Participants : Naznin Fauzia [OSU] , Venmugil Elango [OSU] , Mahesh Ravishankar [OSU] , J. Ramanujam [LSU] , Fabrice Rastello, Atanas Routnev [OSU] , Louis-Noël Pouchet [UCLA] , P. Sadayappan [OSU] .

Emerging computer architectures will feature drastically decreased flops/byte (ratio of peak processing rate to memory bandwidth) as highlighted by recent studies on Exascale architectural trends. Further, flops are getting cheaper while the energy cost of data movement is increasingly dominant. The understanding and characterization of data locality properties of computations is critical in order to guide efforts to enhance data locality.

Reuse distance analysis of memory address traces is a valuable tool to perform data locality characterization of programs. A single reuse distance analysis can be used to estimate the number of cache misses in a fully associative LRU cache of any size, thereby providing estimates on the minimum bandwidth requirements at different levels of the memory hierarchy to avoid being bandwidth bound. However, such an analysis only holds for the particular execution order that produced the trace. It cannot estimate potential improvement in data locality through dependence preserving transformations that change the execution schedule of the operations in the computation.

In this work, we develop a novel dynamic analysis approach to characterize the inherent locality properties of a computation and thereby assess the potential for data locality enhancement via dependence preserving transformations.

This work is the fruit of the collaboration  8.1 with OSU and has been published at ACM TACO'14.