The POP Detector: A Lightweight Online Program Phase Detection Framework

Real-time phase detection enables dynamic adaptation of systems based on different program behavior. Many phase detection techniques have been proposed, with the most successful relating the phases back to application code. In the scope of online phase detection, techniques employ sampling to mitigate the overheads of the phase detection framework. When phase intervals are long enough, sampling approaches perform well. We reopen the question of phase interval length by performing in-depth analysis on the trade-offs between overhead and phase detector performance. We present a new metric which captures the statistical trade-off between phase interval length, phase stability, and the number of phases. We find that while shorter phases perform best in the context of online optimization, existing implementations suffer from performance degradation and overhead at shorter interval sizes. To address this gap, we present the Precise Online Phase (POP) detector. The POP detector utilizes performance counters to build signatures, which are virtually lossless at finer granularity. As a second order, the simplicity of the detector reduces the runtime overhead to just 1.35% and 0.09% at 10M and 100M instruction intervals, respectively.