Sequential and parallel cellular automata-based scheduling algorithms

We present an approach to designing cellular automata-based multiprocessor scheduling algorithms in which extracting knowledge about the scheduling process occurs. We consider the simplest case when a multiprocessor system is limited to two-processors. 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: learning mode and operation mode. 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