Capturing and Composing Parallel Patterns with Intel CnC Ryan Newton Frank Schlimbach Mark Hampton Kathleen Knobe Intel

The most accessible and successful parallel tools today are those that ask programmers to write only isolated serial kernels, hiding parallelism behind a library interface. Examples include Google’s Map-Reduce [5], CUDA [13], and STAPL [12]. This encapsulation approach applies to a wide range of structured, well-understood algorithms, which we call parallel patterns. Today’s highlevel systems tend to encapsulate only a single pattern. Thus we explore the use of Intel CnC as a single framework for capturing and composing multiple patterns.