Optimal Schedules for Parallelizing Anytime Algorithms: The Case of Independent Processes

Lev Finkelstein, Shaul Markovitch, and Ehud Rivlin, Technion

The performance of anytime algorithms having a non-deterministic nature can be improved by solving simultaneously several instances of the algorithm-problem pairs. These pairs may include different instances of a problem (like starting from a different initial state), different algorithms (if several alternatives exist), or several instances of the same algorithm (for non-deterministic algorithms). In this paper we present a general framework for optimal parallelization of independent processes. We show a mathematical model for this framework, present algorithms for optimal scheduling, and demonstrate its usefulness on a real problem.


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