Event quantification provides frequency information for runtime events and is widely used to build fast, secure, and reliable software and systems. It is usually achieved through instrumentation that introduces new instructions into programs but has significant runtime overhead. A key challenge in developing efficient instrumentation is that the instrumentation can barely benefit from modern compiler optimizations because the additional instructions introduce side effects that complicate the optimization process. One intuitive idea to mitigate the side effect issue is to use local counters with transparent lifetimes and scopes instead of relying on global counters. Optimizations are more likely to succeed on instructions that manipulate local variables rather than global ones. However, this idea is not fully adequate, as local counters are unobservable to external users of the quantification data. Therefore, global counters are still necessary, and it is essential to determine when to update them based on the local counters. The challenge is that these updates also introduce side effects that hinder optimizations. We address this update decision problem with the Partially Redundant Update Elimination (PRUE). We present Zircon, the first optimizer-friendly instrumentation to quantify runtime events. With the help of local counters, Zircon separates the event occurrence and the quantification by moving the instructions with side effects outside the scope where the optimizers apply. However, this separation introduces redundant instructions; the additional instructions would still be executed even without event occurrence. In other words, the lifetime control of the local counters challenges the efficiency of instrumentation. PRUE transforms the program and prunes away paths with redundant updates based on static analysis. The evaluation on the SPEC CPU 2017 shows that Zircon is up to 149% faster than state-of-the-art work, including SanCov and Nisse. Unlike existing efforts, Zircon does not compromise accuracy for efficiency; instead, it fully harnesses the power of compilation optimizers and delivers automatic performance gains.

@inproceedings{LingG026,
  title = {Optimizer-Friendly Instrumentation for Event Quantification with PRUE Algorithm},
  author = {Hao Ling and Yiyuan Guo and Charles Zhang},
  booktitle = {ASPLOS},
  year = {2026},
  doi = {10.1145/3779212.3790196}
}