Enhancing Muesli's Data Parallel Skeletons for Multi-Core Computer Architectures
Ciechanowicz Philipp, Kuchen Herbert
Abstract
Algorithmic skeletons encapsulate typical parallel programming patterns such that they can be easily applied by users. Existing skeleton libraries usually work on distributed memory machines. We present an extension of our skeleton library Muesli which now allows to use the same application without modifications on a variety of parallel machines ranging from multi-processor distributed memory to many-core shared memory machines and combinations of those such as clusters of multi-core nodes. Internally, the skeletons are based on MPI and Open MP. We demonstrate the efficiency of our approach by providing experimental results.
Keywords
algorithmic skeletons; parallel programming; multi-core architectures; OPenMP