Abstract
In this paper, we study the general problem of one-dimensional periodic task scheduling under storage requirement, irrespective of machine constraints. We have already presented in [9] a theoretical framework that allows an optimal optimization of periodic storage requirement in a periodic schedule. This problem is used to optimize processor register usage in embedded systems. Our storage optimization problem being NP-complete [8], solving an exact integer linear programming formulation is too expensive in practice. In this article, we propose an efficient two-steps heuristic using model’s properties that allows fast resolution times while providing nearly optimal results. This method includes the resolution of a integer linear program with a totally unimodular constraints matrix in first step, then the resolution of a linear assignment problem. Our solution has been implemented and included inside a compiler for embedded processors.KeywordsInteger Linear ProgramPrecedence ConstraintStorage RequirementPeriodic ScheduleRegister AllocationThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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