Mobile application workloads are inherently driven by user interactions and are characterized by short execution phases and frequent behavioral changes. These properties make it difficult for traditional micro-architecture analysis approaches, which typically assume stable execution behavior, to accurately capture performance bottlenecks in realistic mobile scenarios. To address this challenge, this paper presents CharSPBench, an interaction-aware micro-architecture characterization framework for analyzing mobile benchmarks under representative user interaction scenarios. CharSPBench organizes micro-architecture performance events in a structured and semantically consistent manner. It further enables systematic attribution of performance bottlenecks across different interaction conditions. The framework further supports intensity-based workload analysis to identify workload tendencies, such as memory-intensive and frontend-bound behaviors, under interaction-driven execution. Using the proposed framework, 126 micro-architecture performance events are systematically organized. This process leads to the identification of 19 key, semantically non-redundant features, further grouped into five major micro-architecture subsystems. Based on this structured representation, eight representative interaction-dependent micro-architecture insights are extracted to characterize performance behavior across mobile benchmarks. These quantitative results demonstrate that CharSPBench complements existing micro-architecture analysis techniques and provides practical support for interaction-aware benchmark design and mobile processor performance evaluation.
Ouyang et al. (Mon,) studied this question.