An approach to performance prediction for parallel applications

  • Authors:

  • Engin Ipek;Bronis R. de Supinski;Martin Schulz;Sally A. McKee

  • Affiliations:

  • Computer Systems Lab, School of Electrical and Computer Engineering, Cornell University;Center for Applied Scientific Computing, Lawrence Livermore National Laboratory, Livermore, CA;Center for Applied Scientific Computing, Lawrence Livermore National Laboratory, Livermore, CA;Computer Systems Lab, School of Electrical and Computer Engineering, Cornell University

  • Venue:
  • Euro-Par'05 Proceedings of the 11th international Euro-Par conference on Parallel Processing
  • Year:
  • 2005

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Abstract

Accurately modeling and predicting performance for large-scale applications becomes increasingly difficult as system complexity scales dramatically. Analytic predictive models are useful, but are difficult to construct, usually limited in scope, and often fail to capture subtle interactions between architecture and software. In contrast, we employ multilayer neural networks trained on input data from executions on the target platform. This approach is useful for predicting many aspects of performance, and it captures full system complexity. Our models are developed automatically from the training input set, avoiding the difficult and potentially error-prone process required to develop analytic models. This study focuses on the high-performance, parallel application SMG2000, a much studied code whose variations in execution times are still not well understood. Our model predicts performance on two large-scale parallel platforms within 5%-7% error across a large, multi-dimensional parameter space.