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ABSTRACT

Recent research proposes accelerating processor microarchitecture simulation through statistical sampling. Prior simulation sampling approaches construct accurate model state for each measurement by continuously warming large microarchitectural structures (e.g., caches and the branch predictor) while emulating the billions of instructions between measurements. This approach, called functional warming, occupies hours of runtime while the detailed simulation that is measured requires mere minutes.To eliminate the functional warming bottleneck, we propose TurboSMARTS, a simulation framework that stores functionally-warmed state in a library of small, reusable checkpoints. TurboSMARTS enables the creation of the thousands of checkpoints necessary for accurate sampling by storing only the subset of warmed state accessed during simulation of each brief execution window. TurboSMARTS matches the accuracy of prior simulation sampling techniques (i.e., ±3% error with 99.7% confidence), while estimating the performance of an 8-way out-of-order super-scalar processor running SPEC CPU2000 in 91 seconds per benchmark, on average, using a 12 GB checkpoint library.

REFERENCES

• 1: Roland E. Wunderlich , Thomas F. Wenisch , Babak Falsafi , James C. Hoe, SMARTS: accelerating microarchitecture simulation via rigorous statistical sampling, Proceedings of the 30th annual international symposium on Computer architecture, June 09-11, 2003, San Diego, California [doi>10.1145/859618.859629]

INDEX TERMS

Primary Classification:
  C. Computer Systems Organization
  C.4 PERFORMANCE OF SYSTEMS
      Subjects: Measurement techniques

Additional Classification:
  B. Hardware
  B.8 Performance and Reliability
      B.8.2 Performance Analysis and Design Aids

  C. Computer Systems Organization
  C.1 PROCESSOR ARCHITECTURES
      C.1.1 Single Data Stream Architectures
  C.4 PERFORMANCE OF SYSTEMS
      Subjects: Modeling techniques

General Terms:
Design, Measurement, Performance

Keywords:
checkpointed microarchitecture simulation, simulation sampling