This paper presents a scalable, statement-level visualization that shows related code in a way that supports human interpretation of clustering and context. The visualization is applicable to many software-engineering tasks through the utilization and visualization of problem-specific meta-data. The visualization models statement-level code relations from a system-dependence-graph model of the program being visualized. Dynamic, run-time information is used to augment the static program model to further enable visual cluster identification and interpretation. In addition, we performed a user study of our visualization on an example program domain. The results of the study show that our new visualization successfully revealed relevant context to the programmer participants.