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A. Bhattacharyya, T. Hoefler:

 PEMOGEN: Automatic Adaptive Performance Modeling During Program Runtime

(In Proceedings of the 23rd International Conference on Parallel Architectures and Compilation (PACT'14), presented in Edmonton, Alberta, Canada, pages 393-404, ACM, ISBN: 978-1-4503-2809-8, Aug. 2014)


Traditional means of gathering performance data are tracing, which is limited by the available storage, and profiling, which has limited accuracy. Performance modeling is often used to interpret the tracing data and generate performance predictions. We aim to complement the traditional data collection mechanisms with online performance modeling, a method that generates performance models while the application is running. This allows us to greatly reduce the storage overhead while still producing accurate predictions. We present PEMOGEN, our compilation and modeling framework that automatically instruments applications to generate performance models during program execution. We demonstrate the ability of PEMOGEN to both reduce storage cost and improve the prediction accuracy compared to traditional techniques such as least squares fitting. With our tool, we automatically detect 3,370 kernels from fifteen NAS and Mantevo applications and model their execution time with a median coefficient of variation R2 of 0.81. These automatically generated performance models can be used to quickly assess the scaling and potential bottlenecks with regards to any input parameter and the number of processes of a parallel application.


download article:


  author={A. Bhattacharyya and T. Hoefler},
  title={{PEMOGEN: Automatic Adaptive Performance Modeling During Program Runtime}},
  booktitle={Proceedings of the 23rd International Conference on Parallel Architectures and Compilation (PACT'14)},
  location={Edmonton, Alberta, Canada},

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