Approximate graph clustering for program characterization

Abstract

An important aspect of system optimization research is the discovery of program traits or behaviors. In this paper, we present an automated method of program characterization which is able to examine and cluster program graphs, i.e., dynamic data graphs or control flow graphs. Our novel approximate graph clustering technology allows users to find groups of program fragments which contain similar code idioms or patterns in data reuse, control flow, and context. Patterns of this nature have several potential applications including development of new static or dynamic optimizations to be implemented in software or in hardware. For the SPEC CPU 2006 suite of benchmarks, our results show that approximate graph clustering is effective at grouping behaviorally similar functions. Graph based clustering also produces clusters that are more homogeneous than previously proposed non-graph based clustering methods. Further qualitative analysis of the clustered functions shows that our approach is also able to identify some frequent unexploited program behaviors. These results suggest that our approximate graph clustering methods could be very useful for program characterization.