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Publications of SPCL
|A. Bhattacharyya, G. Kwasniewski, T. Hoefler:|
|Using Compiler Techniques to Improve Automatic Performance Modeling|
(presented in San Francisco, CA, USA, ACM, Oct. 2015, Accepted at the 24th International Conference on Parallel Architectures and Compilation (PACT'15) )
AbstractPerformance 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 dynamic approach 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 for 4.3% (maximum 10%) and can reduce the overhead 25% (maximum 65%) as compared to previous approaches.