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Schedulability-guided exploration of multi-core systems

机译：可调度性指导的多核系统探索

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摘要

Efficient mapping of tasks onto heterogeneous multi-core systems is very challenging especially under hard timing constraints. Assigning tasks to processors is an NP-hard problem and solving it requires the use of meta-heuristics. Relevantly, genetic algorithms have already proven to be one of the most powerful and widely used stochastic tools to solve this problem. Conventional genetic algorithms were initially defined as a general evolutionary algorithm based on blind operators. It is commonly admitted that the use of these operators is quite poor for an efficient exploration. Likewise, since exhaustive exploration of the solution space is unrealistic, a potent option is often to guide the exploration process by hints, derived by problem structure. This guided exploration prioritizes fitter solutions to be part of next generations and avoids exploring unpromising configurations by transmitting a set of predefined criteria from parents to children. Consequently, genetic operators, such as crossover, must incorporate specific domain knowledge to intelligently guide the exploration of the solution space. In this paper, we illustrate and evaluate the impact of crossover operators and we propose a hybrid genetic algorithm based on a novel schedulability-guided operator that easily outperforms the classical operators by offering at least 21% improvement in terms of ratio of certainly schedulable tasks.
机译：任务到异构多核系统上的有效映射非常具有挑战性，尤其是在严格的时间限制下。将任务分配给处理器是一个NP难题，要解决该问题，需要使用元启发法。相应地，遗传算法已被证明是解决这一问题的最强大，使用最广泛的随机工具之一。最初，传统的遗传算法被定义为基于盲算子的通用进化算法。通常认为，对于有效的勘探，使用这些算子的能力很差。同样，由于彻底探索解决方案空间是不现实的，因此有效的选择通常是通过提示（由问题结构得出）来指导探索过程。该指导性探索优先考虑将钳工解决方案作为下一代解决方案的一部分，并通过将一组预定义的标准从父母传递给孩子来避免探索毫无希望的配置。因此，遗传算子（例如交叉算子）必须结合特定领域的知识，以智能地指导解决方案空间的探索。在本文中，我们说明并评估了交叉算子的影响，并提出了一种基于新型可调度性指导算子的混合遗传算法，该算法通过在肯定可调度任务的比率方面提供至少21％的提升，从而轻松胜过传统算子。

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