将聚类抽样与单次抽样相结合,设计有效的抽样方案

Paul D. Bryan, T. Conte
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引用次数: 5

摘要

微架构模拟比本地执行要慢几个数量级。随着越来越多的元素被精确建模,与缓慢模拟相关的问题将进一步加剧。考虑到这些问题,许多研究人员设计了采样技术来减少模拟时间。当使用聚类抽样技术时,必须注意去除抽样和非抽样偏差。研究者们已经设计出了一些巧妙的方法来有效地减少非抽样偏差,但对于有效地减少抽样偏差(抽样方案设计)的研究却很少。传统上,采样方案设计是一个迭代过程,需要全工作量模拟进行误差比较。本研究提出了一种单次模拟采样方案设计技术。使用这种方法,可以同时评估数千个采样方案候选。使用这种技术,相对于总工作负载模拟,仿真速度平均提高了17倍,最大提高了73倍。此外,该技术允许用户在不运行整个工作负载的情况下有效地估计样本误差。
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Combining cluster sampling with single pass methods for efficient sampling regimen design
Microarchitectural simulation is orders of magnitude slower than native execution. As more elements are accurately modeled, problems associated with slow simulation are further exacerbated. Given these issues, many researchers have devised sampling techniques to reduce simulation time. When cluster sampling techniques are used, care must be taken to remove sampling and non-sampling biases. Researchers have devised clever methods for effectively reducing non-sampling bias, but little work has been proposed for efficient reduction of sampling bias (sampling regimen design). Traditionally, sampling regimen design has been an iterative process that required a full workload simulation for error comparison. In this study, a single-pass simulation technique for sampling regimen design is proposed. Using this method, thousands of sampling regimen candidates can be simultaneously evaluated. With this technique, simulation speed was increased by an average factor of 17 with a maximum increase of 73 times relative to the total workload simulation. Additionally, this technique allows the user to effectively estimate the sample error without running the entire workload.
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