Adaptive simulation sampling using an autoregressive framework

Document Type

Conference Proceeding

Date of Original Version



Software simulators remain several orders of magnitude slower than the modern microprocessor architectures they simulate. Although various reduced-time simulation tools are available to accurately help pick truncated benchmark simulation, they either come with a need for offline analysis of the benchmarks initially or require many iterative runs of the benchmark. In this paper, we present a novel sampling simulation method, which only requires a single run of the benchmark to achieve a desired confidence interval, with no offline analysis and gives comparable results in accuracy and sample sizes to current simulation methodologies. Our method is a novel configuration independent approach that incorporates an Autoregressive (AR) model using the squared coefficient of variance (SCV) of Cycles per Instruction (CPI). Using the sampled SCVs of past intervals of a benchmark, the model computes the required number of samples for the next interval through a derived relationship between number of samples and the SCVs of the CPI distribution. Our implementation of the AR model achieves an actual average error of only 0.76% on CPI with a 99.7% confidence interval of ±0.3% for all SPEC2K benchmarks while simulating, in detail, an average of 40 million instructions per benchmark. © 2009 IEEE.

Publication Title

Proceedings - 2009 International Conference on Embedded Computer Systems: Architectures, Modeling and Simulation, IC-SAMOS 2009