Performance-sensitivity and performance-similarity based workload reduction

In computer design area, pre-silicon early-stage design exploration requires the detailed simulation which is running applications on a cycle-level microprocessor simulator. Main objectives of simulation-level design exploration include understanding the architectural behaviors of target applications and finding optimal configurations to cover wide range of applications in terms of performance and power. However, full simulation of an industry standard benchmark suite takes several weeks to several months. Among many techniques for reducing simulation time, a tool called SimPoint is popularly used. Even though, simulation load with the reduced workloads by SimPoint is still heavy considering design complexity of modern microprocessors.

Basic motivation of this research is from how design exploration is actually performed. Designers will observe the performance impact from resource variations or configuration changes. If a simulation point shows low sensitivity to resource variations, designers would eliminate those simulation points from the simulation setup procedure. In this thesis, we focus on identifying those simulation points which have high sensitivity or low sensitivity, by which overall simulation methodology can be effectively improved. We also performed the performance sensitivity-based similarity analysis for workload reduction by using statistical technique. Our experiment results show that the proposed scheme provides around 50% reduction with 0.2%--3.5% CPI error rate over the original SimPoint method.