Dynamic task mapping onto multi-core architectures through stream rewriting

Abstract

Task graphs provide an efficient model of computation for specification, analysis, and implementation of concurrent applications. In this paper, we present a novel approach for mapping the class of series-parallel task graphs onto multi-core architectures based on pattern matching. Both the topology of the graph and the state of the tasks are encoded as a stream of tokens, which is iteratively rewritten at multiple positions in parallel. Hence, our technique is most useful for compute-intensive applications that must adapt to frequently varying and unpredictable workload at runtime. Several complex examples have been evaluated on a multi-core architecture and the experimental results show the effectiveness of our approach.