2015 International Conference on Parallel Architecture and Compilation (PACT) (2015)
San Francisco, CA, USA
Oct. 18, 2015 to Oct. 21, 2015
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/PACT.2015.39
Performance modeling can be utilized in a number of scenarios, starting from finding performance bugs to the scalability study of applications. Existing dynamic and static approaches for automating the generation of performance models have limitations for precision and overhead. In this work, we explore combination of a number of static and dynamic analyses for life-long performance modeling and investigate accuracy, reduction of the model search space, and performance improvements over previous approaches on a wide range of parallel benchmarks. We develop static and dynamic schemes such as kernel clustering, batched model updates and regulation of modeling frequency for reducing the cost of measurements, model generation, and updates. Our hybrid approach, on average can improve the accuracy of the performance models by 4.3%(maximum 10%) and can reduce the overhead by 25% (maximum 65%) as compared to previous approaches.
Analytical models, Predictive models, Mathematical model, Kernel, Computational modeling, Frequency measurement, Adaptation models,Static Analysis, Performance Modeling, LASSO Regression
Arnamoy Bhattacharyya, Grzegorz Kwasniewski, Torsten Hoefler, "Using Compiler Techniques to Improve Automatic Performance Modeling", 2015 International Conference on Parallel Architecture and Compilation (PACT), vol. 00, no. , pp. 468-479, 2015, doi:10.1109/PACT.2015.39