Performance challenges in modular parallel programs

Abstract

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 granularity control, which requires the programmer to determine when and how parallel tasks should be sequentialized.