Computer system simulation studies routinely rely on executing a limited number of short application regions, since full end-to-end simulation is prohibitively time-consuming. To preserve representativeness, existing methods employ either random sampling or phase-based characterization to identify representative regions. In this work, we revisit random sampling in the context of computer architecture simulation. To assess how the confidence level varies with different micro-architectural configurations, we examine how the sample standard deviation relates to the sample mean. We show that the ranked set sampling (RSS) technique - well established in the statistical literature - maps naturally to architectural simulation and yields significantly tighter confidence intervals than simple random sampling. Across our experiments, RSS reduces the confidence interval width by up to 50%. We further introduce a repeated subsampling scheme that identifies representative simulation regions for future studies. For a fixed sample size, this approach reduces the maximum observed error from 35% to 10%. Evaluating two selection criteria, we find that more informed subsample selection provides additional accuracy gains. Overall, our method achieves an average error below 2% and a maximum error of 3.5% across individual SPEC CPU 2017 Integer applications when simulating 30 regions of 1 million instructions each.

翻译：计算机系统仿真研究通常依赖执行有限数量的短程序片段，这是由于完整的端到端仿真耗时长到难以承受。为保持代表性，现有方法采用随机抽样或基于阶段的特征提取来识别有代表性的程序片段。本研究重新审视了计算机体系结构仿真中的随机抽样方法。为评估不同微体系结构配置下置信水平的变化，我们检验了样本标准差与样本均值之间的关系。研究表明，统计学文献中成熟的排序集抽样技术天然适用于体系结构仿真，且产生的置信区间宽度远小于简单随机抽样。在实验中，排序集抽样将置信区间宽度缩减高达50%。我们进一步提出一种重复子抽样方案，可为后续研究识别代表性仿真区域。在固定样本容量下，该方法将最大观测误差从35%降至10%。对两种选取准则的评估显示，更信息化的子样本选择能进一步带来精度提升。总体而言，当对每个SPEC CPU 2017整型数应用模拟30个含百万指令的程序片段时，本方法平均误差低于2%，最大误差为3.5%。