C4.5: programs for machine learning
C4.5: programs for machine learning
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Proceedings of a symposium on Compiler optimization
gSpan: Graph-Based Substructure Pattern Mining
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
CloseGraph: mining closed frequent graph patterns
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Combining Pattern Classifiers: Methods and Algorithms
Combining Pattern Classifiers: Methods and Algorithms
A First-Order Superscalar Processor Model
Proceedings of the 31st annual international symposium on Computer architecture
Frequent Substructure-Based Approaches for Classifying Chemical Compounds
IEEE Transactions on Knowledge and Data Engineering
Graph mining: Laws, generators, and algorithms
ACM Computing Surveys (CSUR)
Measuring Benchmark Similarity Using Inherent Program Characteristics
IEEE Transactions on Computers
Performance prediction based on inherent program similarity
Proceedings of the 15th international conference on Parallel architectures and compilation techniques
Efficient architectural design space exploration via predictive modeling
ACM Transactions on Architecture and Code Optimization (TACO)
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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.