Efficient architectural design space exploration via predictive modeling

  • Authors:

  • Engin Ipek;Sally A. McKee;Karan Singh;Rich Caruana;Bronis R. de Supinski;Martin Schulz

  • Affiliations:

  • Cornell University, Ithaca, New York;Cornell University, Ithaca, New York;Cornell University, Ithaca, New York;Cornell University, Ithaca, New York;Lawrence Livermore National Laboratory, Livermore, California;Lawrence Livermore National Laboratory, Livermore, California

  • Venue:
  • ACM Transactions on Architecture and Code Optimization (TACO)
  • Year:
  • 2008

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Abstract

Efficiently exploring exponential-size architectural design spaces with many interacting parameters remains an open problem: the sheer number of experiments required renders detailed simulation intractable. We attack this via an automated approach that builds accurate predictive models. We simulate sampled points, using results to teach our models the function describing relationships among design parameters. The models can be queried and are very fast, enabling efficient design tradeoff discovery. We validate our approach via two uniprocessor sensitivity studies, predicting IPC with only 1--2% error. In an experimental study using the approach, training on 1% of a 250-K-point CMP design space allows our models to predict performance with only 4--5% error. Our predictive modeling combines well with techniques that reduce the time taken by each simulation experiment, achieving net time savings of three-four orders of magnitude.