It is well known that most serial programs exhibit time varying behavior, for example, alternating between memory- and compute-bound phases. However, most research into program phase behavior has focused on the serial SPEC benchmark suite, with little investigations into large scale phase behavior in parallel applications. In this study we compare and examine the time-varying behavior of the SPEC2006 (serial) and the PARSEC 2.1 (parallel) benchmarks suites, and investigate the program phase behavior found in parallel applications with different parallelization models. To this end, we extend a general purpose runtime phase desection library to handle parallel applications. Our results reveal that serial applications have significantly more program phases (2.4x) with larger variation in CPI (1.5x) compared to parallel applications. While the number of phases are fewer in parallel applications, there still exists interesting phase behavior. In particular, we find that data-parallel applications have shorter phases with more threads. This makes phase-guided runtime optimizations (e.g., dynamic voltage frequency scaling) less attractive as the number of threads grows. Meaning it is much more difficult to exploit runtime optimizations in parallel applications.