Predicting Objectives on a Reduced Search Space of Multiobjective Function Inlining
Predicting Objectives on a Reduced Search Space of Multiobjective Function Inlining
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DOI：
10.1145/3493229.3493303
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发表时间：
2021-11
期刊：
影响因子：
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通讯作者：
Kateryna Muts;H. Falk
中科院分区：
文献类型：
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作者：
Kateryna Muts;H. Falk
最坏情况执行时间（WCET）、能量消耗和代码大小是硬实时系统最重要的标准之一。为了在编译时估计WCET和能耗，通常使用静态分析器：它们通过调用耗时的微架构、数据流和控制流分析来估计目标。昂贵的分析使得在编译时使用进化算法来解决这两个目标的多目标问题几乎是不可行的，因为任何进化算法都会广泛地评估目标来找到解决方案。我们提出了一种方法，加快了进化算法提供一个减少的搜索空间和预测模型适合减少搜索空间，所以该算法需要探索一个较小的搜索空间，可以使用快速预测，而不是耗时的估计来评估WCET和能源消耗。所提出的方法是通用的，足以用于任何基于编译器的优化。我们证明了它的优点，解决多目标函数内联问题在编译时。
The Worst-Case Execution Time (WCET), energy consumption, and code size are among the most important criteria of hard real-time systems. To estimate the WCET and energy consumption at compile time, static analyzers are often used: they estimate the objectives by invoking time-consuming microarchitecture, data flow, and control flow analyses. The expensive analyses make it almost infeasible to use evolutionary algorithms for solving multiobjective problems with these two objectives at compile time, since any evolutionary algorithm extensively evaluates objectives to find solutions. We propose a method that speeds up an evolutionary algorithm supplying it with a reduced search space and prediction model fitted on the reduced search space, so the algorithm needs to explore a smaller search space and can use fast predictions instead of time-consuming estimations to evaluate the WCET and energy consumption. The proposed approach is general enough to be used for any compiler-based optimization. We demonstrate the advantages of it solving a multiobjective function inlining problem at compile time.