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 serial kernels, hiding parallelism behind a library interface. Examples include Google's Map-Reduce, CUDA, and STAPL. This encapsulation approach applies to a wide range of structured, well-understood algorithms, which the authors call parallel patterns. Today's high-level systems tend to encapsulate only a single pattern. Thus they explore the use of Intel CnC as a single framework for capturing and composing multiple patterns.

Provided by: Intel Topic: Software Date Added: Aug 2011 Format: PDF

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