Compiler-based WCET prediction performing function specialization
Compiler-based WCET prediction performing function specialization
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DOI：
10.1145/3378678.3391879
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发表时间：
2020-05
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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）是衡量硬实时系统性能的重要指标之一。已经提出了许多优化来改善嵌入式应用程序在编译时的WCET。此外，由于现代嵌入式系统还必须满足额外的设计标准，例如，代码大小或能量消耗，编译器的优化更经常地朝向多目标优化问题。进化算法是求解多目标问题最常用的方法。为了找到目标之间的最佳折衷的集合，任何进化算法都需要对目标函数进行广泛的评估。因此，在许多情况下，将WCET视为多目标问题中的目标是不可行的，因为编译时的WCET分析可能非常耗时。出于这个原因，我们提出了一种基于机器学习技术的方法来预测WCET在编译时的值。一个著名的基于编译器的优化，功能专业化，被认为是一个基地，所提出的预测模型。回归方法进行了分析，使WCET预测尽可能精确地执行功能专业化。
The Worst-Case Execution Time (WCET) is one of the most important criteria of hard real-time systems. Many optimizations have been proposed to improve WCET of an embedded application at compile time. Moreover, since modern embedded systems must also satisfy the additional design criteria like, e.g., code size or energy consumption, more often the compiler's optimizations go towards multi-objective optimization problems. Evolutionary algorithms are the most widely used method to solve a multi-objective problem. In order to find the set of the best trade-offs between the objectives, any evolutionary algorithm requires extensive evaluations of the objective functions. Thus, considering WCET as an objective in a multi-objective problem is infeasible in many cases, because the WCET analysis at compile time can be very time-consuming. For this reason, we propose a method based on a machine learning technique to predict the values of WCET at compile time. A well-known compiler-based optimization, function specialization, is considered as a base for the proposed prediction model. A regression method is analyzed in terms of making WCET predictions as precise as possible performing function specialization.