Over the past decade, many programming languages and systems for parallel-computing have been developed, including Cilk, Fork/Join Java, Habanero Java, Parallel Haskell, Parallel ML, and X10. Although these systems raise the level of abstraction at which parallel code are written, performance continues to require the programmer to perform extensive optimizations and tuning, often by taking various architectural details into account. One such key optimization is gran-ularity control, which requires the programmer to determine when and how parallel tasks should be sequentialized. In this paper, we briefly describe some of the challenges associated with automatic granularity control when trying to achieve portable performance for parallel programs with arbitrary nesting of parallel constructs. We consider a result from the functional-programming community, whose starting point is to consider an "oracle" that can predict the work of parallel codes, and thereby control granularity. We discuss the challenges in implementing such an oracle and proving that it has the desired theoretical properties under the nested-parallel programming model. Context The proliferation of multicore hardware in the past decade has brought shared-memory parallelism into the mainstream. This change has led to much research on implicit threading, a.k.a. implicit parallelism, which seeks to make parallel programming easier by delegating certain tedious but important details, such as the scheduling of parallel tasks to the compiler and the run-time system. Implementations include: OpenMP, Cilk, TBB, X10, parallel ML.