Abstract-Increasing integrated circuit (IC) power densities and temperatures may hamper multiprocessor system-on-chip (MPSoC) use in hard real-time systems. This article formalizes the temperature-aware real-time MPSoC assignment and scheduling problem and presents an optimal mixed integer linear programming based solution that considers the impact of scheduling and assignment decisions on MPSoC thermal profiles to directly minimize the chip peak temperature. We also introduce a flexible heuristic framework for task assignment and scheduling that permits system designers to trade off accuracy for running time to solve large problem instances. Finally, for task sets with sufficient slack, we show that inserting idle times between task executions can further reduce the peak temperature of the MPSoC quite significantly.
Increasing power densities and the high cost of low thermal resistance packages and cooling solutions make it impractical to design processors for worst-case temperature scenarios. As a result, packages and cooling solutions are designed for less than worst-case power densities and dynamic voltage and frequency scaling (DVFS) is used to prevent dangerous on-chip temperatures at run time. Unfortunately, DVFS can cause unpredicted drops in performance (e.g., long response times). We propose and optimally solve the problem of thermally-constrained online work maximization for general-purpose computing systems on uniprocessors with discrete speed levels and non-negligible transition overheads. Simulation results show that our approach completes 47.7% on average and up to 68.0% more cycles than a naïve policy.
Abstract-The elastic task model is a powerful model for adapting periodic real-time systems in the presence of uncertainty. This paper generalizes the existing elastic scheduling approach in several directions. It reveals that the original task compression algorithm in fact solves a quadratic programming problem that seeks to minimize the sum of the squared deviation of a task's utilization from initial desired utilization. This finding indicates that the task compression algorithm may be applied to efficiently solve other similar types of problems that often arise in real-time applications. In particular, an iterative approach is proposed to solve the task compression problem for real-time tasks with deadlines less than respective periods. Furthermore, the framework is generalized to adjust task deadlines instead of task periods.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.