Adaptive simulation sampling using an Autoregressive framework

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 plusmn0.3% for all SPEC2K benchmarks while simulating, in detail, an average of 40 million instructions per benchmark.