A critique of cyclomatic complexity as a software metric
Software Engineering Journal
C4.5: programs for machine learning
C4.5: programs for machine learning
Analysis of benchmark characteristics and benchmark performance prediction
ACM Transactions on Computer Systems (TOCS)
The program dependence graph in a software development environment
SDE 1 Proceedings of the first ACM SIGSOFT/SIGPLAN software engineering symposium on Practical software development environments
Proceedings of a symposium on Compiler optimization
Combining Pattern Classifiers: Methods and Algorithms
Combining Pattern Classifiers: Methods and Algorithms
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
ACM SIGARCH Computer Architecture News
Characterization of file I/O activity for SPEC CPU2006
ACM SIGARCH Computer Architecture News
Predicting parallel application performance via machine learning approaches: Research Articles
Concurrency and Computation: Practice & Experience - Parallel and Distributed Computing (EuroPar 2005)
IEEE Transactions on Software Engineering
Efficient architectural design space exploration via predictive modeling
ACM Transactions on Architecture and Code Optimization (TACO)
Architecture performance prediction using evolutionary artificial neural networks
Evo'08 Proceedings of the 2008 conference on Applications of evolutionary computing
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The decision which hardware platform to use for a certain application is an important problem in computer architecture. This paper reports on a study where a data-mining approach is used for this decision. It relies purely on source-code characteristics, to avoid potentially expensive programme executions. One challenge in this context is that one cannot infer how often functions that are part of the application are typically executed. The main insight of this study is twofold: (a) Source-code characteristics are sufficient nevertheless. (b) Linking individual functions with the runtime behaviour of the programme as a whole yields good predictions. In other words, while individual data objects from the training set may be quite inaccurate, the resulting model is not.