@article{7257138,
  abstract     = {{Optimizing processors for (a) specific application(s) can substantially improve energy-efficiency. With the end of Dennard scaling, and the corresponding reduction in energy-efficiency gains from technology scaling, such approaches may become increasingly important. However, designing application-specific processors requires fast design space exploration tools to optimize for the targeted application(s). Analytical models can be a good fit for such design space exploration as they provide fast performance and power estimates and insight into the interaction between an application’s characteristics and the micro-architecture of a processor. Unfortunately, prior analytical models for superscalar out-of-order processors require micro-architecture dependent inputs, such as cache miss rates, branch miss rates and memory-level parallelism. This requires profiling the applications for each cache and branch predictor configuration of interest, which is far more time-consuming than evaluating the analytical performance models. In this work we present a micro-architecture independent profiler and associated analytical models that allow us to produce performance and power estimates across a large superscalar out-of-order processor design space almost instantaneously. We show that using a micro-architecture independent profile leads to a speedup of 300 compared to detailed simulation for our evaluated design space. Over a large design space, the model has a 9.3% average error for performance and a 4.3% average error for power, compared to detailed cycle-level simulation. The model is able to accurately determine the optimal processor configuration for different applications under power or performance constraints, and provides insight into performance through cycle stacks.}},
  author       = {{Van den Steen, Sam and Eyerman, Stijn and De Pestel, Sander and Mechri, Moncef and Carlson, Trevor and Black-Schaffer, David and Hagersten, Erik and Eeckhout, Lieven}},
  issn         = {{0018-9340}},
  journal      = {{IEEE TRANSACTIONS ON COMPUTERS}},
  keywords     = {{Micro-architecture,Analytical model,Performance,Power}},
  language     = {{eng}},
  number       = {{12}},
  pages        = {{3537--3551}},
  publisher    = {{IEEE}},
  title        = {{Analytical processor performance and power modeling using micro-architecture independent characteristics}},
  url          = {{http://dx.doi.org/10.1109/TC.2016.2547387}},
  volume       = {{65}},
  year         = {{2016}},
}

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