In this paper, we explore the use of hierarchically structured multiprocessor tasks (M-tasks) for programming multi-core cluster systems.These systems often have hierarchically structured interconnection networks combining different computing resources, starting with the interconnect within multi-core processors up to the interconnection network combining nodes of the cluster or supercomputer. M-task programs can support the effective use of the computing resources by adapting the task structure of the program to the hierarchical organization of the cluster system and by exploiting the available data parallelism within the M-tasks. In particular, we consider different mapping algorithms for M-tasks and investigate the resulting efficiency and scalability. We present experimental results for different application programs and different multi-core systems.
['Jörg Dümmler', 'Thomas Rauber', 'Gudula Rünger']
Mapping Algorithms for Multiprocessor Tasks on Multi-Core Clusters