Selecting computer architectures by means of control-flow-graph mining

Abstract

Deciding which computer architecture provides the best performance for a certain program is an important problem in hardware design and benchmarking. While previous approaches require expensive simulations or program executions, we propose an approach which solely relies on program analysis. We correlate substructures of the control-flow graphs representing the individual functions with the runtime on certain systems. This leads to a prediction framework based on graph mining, classification and classifier fusion. In our evaluation with the SPEC CPU 2000 and 2006 benchmarks, we predict the faster system out of two with high accuracy and achieve significant speedups in execution time. © 2009 Springer Berlin Heidelberg.