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Proceedings of the 22nd International Conference on Parallel Architectures and Compilation Techniques (2001)
Barcelona, Spain
Sept. 8, 2001 to Sept. 12, 2001
ISBN: 0-7695-1363-8
pp: 0015
Sébastien Nussbaum , University of Wisconsin - Madison
James E. Smith , University of Wisconsin - Madison
ABSTRACT
Abstract: Statistical simulation is a technique for fast performance evaluation of superscalar processors. First, intrinsic statistical information is collected from a single detailed simulation of a program. This information is then used to generate a synthetic instruction trace that is fed to a simple processor model, along with cache and branch prediction statistics. Because of the probabilistic nature of the simulation, it quickly converges to a performance rate. The simplicity and simulation speed make it useful for fast design space exploration; as such, it is a good complement to conventional detailed simulation. The accuracy of this technique is evaluated for different levels of modeling complexity. Both errors and convergence properties are studied in detail. A simple instruction model yields an average error of 8% compared with detailed simulation. A more detailed instruction model reduces the error to 5% but requires about three times as long to converge.
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CITATION
Sébastien Nussbaum, James E. Smith, "Modeling Superscalar Processors via Statistical Simulation", Proceedings of the 22nd International Conference on Parallel Architectures and Compilation Techniques, vol. 00, no. , pp. 0015, 2001, doi:10.1109/PACT.2001.953284
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