Scheduling parallel program tasks onto arbitrary target machines
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In this paper, we present a novel approach to designing cellular automata-based multiprocessor scheduling algorithms in which extracting knowledge about the scheduling process occurs. This knowledge can potentially be used while solving new instances of the scheduling problem. We consider the simplest case when a multiprocessor system is limited to two-processors, but we do not imply any limitations on the size and parameters of parallel programs. To design cellular automata corresponding to a given program graph, we propose a generic definition of program graph neighborhood, transparent to the various kinds, sizes, and shapes of program graphs. The cellular automata-based scheduler works in two modes. In learning mode we use a genetic algorithm to discover rules of cellular automata suitable for solving instances of a scheduling problem. In operation mode, discovered rules of cellular automata are able to automatically find an optimal or suboptimal solution of the scheduling problem for any initial allocation of a program graph in two-processor system graph. Discovered rules are typically suitable for sequential cellular automata working as a scheduler, while the most interesting and promising feature of cellular automata are their massive parallelism. To overcome difficulties in evolving parallel cellular automata rules, we propose using coevolutionary genetic algorithm. Discovered this way, rules enable us to design effective parallel schedulers. We present a number of experimental results for both sequential and parallel scheduling algorithms discovered in the context of a cellular automata-based scheduling system.