GENETIC ALGORITHM FOR MAPPING TASKS ONTO TO RECONFIGURABLE PARALLEL PROCESSOR

The authors describe a genetic algorithm for a difficult optimisation problem which arises in the context of parallel processing. The problem is to assign each task in the given task graph T to a processor, so as to minimise the total overall execution time of the tasks. Total execution time is computed with the knowledge of individual run times of tasks and the communication requirements among tasks. The intertask communication time is dependent on the interconnection network which connects the processors. No prior knowledge of the interconnection topology is assumed. The algorithm finds the interconnection architecture that is best suited for the task graph T; this makes sense when the target architecture is reconfigurable through programmable switches, e.g. transputer-based parallel processors. The algorithm is also extended to add heterogeneous platforms, where each task t can be executed on a particular class of processors. The optimisation technique is based on the genetic paradigm. The authors describe an efficient chromosome representation, genetic operators and a fitness measure suitable for the application.