Paper Info
Title
Beyond reuse distance analysis: Dynamic analysis for characterization of data locality potential.
Abstract
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 article, 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. The execution trace of a code is analyzed to extract a Computational-Directed Acyclic Graph (CDAG) of the data dependences. The CDAG is then partitioned into convex subsets, and the convex partitioning is used to reorder the operations in the execution trace to enhance data locality. The approach enables us to go beyond reuse distance analysis of a single specific order of execution of the operations of a computation in characterization of its data locality properties. It can serve a valuable role in identifying promising code regions for manual transformation, as well as assessing the effectiveness of compiler transformations for data locality enhancement. We demonstrate the effectiveness of the approach using a number of benchmarks, including case studies where the potential shown by the analysis is exploited to achieve lower data movement costs and better performance.
Year
DOI
Venue
2014
10.1145/2541228.2555309
ACM Transactions on Architecture and Code Optimization (TACO)
Keywords
DocType
Volume
data dependence,dynamic analysis,reuse distance analysis,data locality,lower data movement cost,execution trace,data locality potential,data locality property,data locality characterization,data movement,data locality enhancement,dependence-preserving transformation,compiler,data flow graph,profiling
Journal
abs/1401.5024
Issue
ISSN
Citations 
4
1544-3566
4
PageRank 
References 
Authors
0.42
35
8
Name
Order
Citations
PageRank
Naznin Fauzia1542.04
Venmugil Elango2192.35
Mahesh Ravishankar3855.24
J. Ramanujam4129775.35
Fabrice Rastello548238.30
Atanas Rountev61999108.60
Louis-noël Pouchet788047.61
P. Sadayappan84821344.32