Capturing and Composing Parallel Patterns with Intel CnC

The most accessible and successful parallel tools today are those that ask programmers to write only isolated se- rial kernels, hiding parallelism behind a library interface. Examples include Google's Map-Reduce (5), CUDA (13), and STAPL (12). This encapsulation approach ap- plies to a wide range of structured, well-understood al- gorithms, which we call parallel patterns. Today's high- level systems tend to encapsulate only a single pattern. Thus we explore the use of Intel CnC as a single frame- work for capturing and composing multiple patterns.