Agent modeling method for multi-objective compilation optimization sequence selection

Abstract

The invention provides an agent modeling method for multi-objective compilation optimization sequence selection, aims to provides a solution for solving a calculation cost constraint problem of the multi-objective compilation optimization sequence selection and belongs to the field of compiler optimization. The method comprises the following steps of: firstly, performing binary coding on a compilation optimization sequence; respectively designing fitness functions for the scale and the running speed of two optimization target codes; generating a child population after selection and crossover operations; combining the child population with a parent population; performing quick non-dominated sorting to generate a new population; and finally obtaining a Pareto optimal solution set. In the search iteration process, the compilation optimization sequence and the two corresponding target fitness values are used for constructing an agent model, the agent model is used for calculating approximate fitness values for the child population generated by evolution operation, and actual fitness values are calculated for excellent solutions, so that the evolution efficiency is improved. According to the method, the compilation optimization sequence meeting multiple objectives (such as running speed and code scale) can be effectively selected for a program to be compiled, and the problem of calculation cost constraint caused in the iteration process is solved.

Description

technical field [0001] The invention belongs to the technical field of compiler optimization, and relates to a proxy modeling method for the calculation cost constraint problem in multi-objective compilation optimization sequence selection. Background technique [0002] In the actual development process, the compiled target machine code needs to have relatively ideal performance, such as small executable code size and fast execution speed. However, for the compiled target machine code, code size and running speed are optimization goals that have certain conflicts. If the program running speed is pursued, the program scale will be enlarged. If the pursuit of smaller executable code size, it will affect the running speed of the program. Therefore, how to select an appropriate compilation optimization sequence for the program to be compiled so that it can achieve a trade-off between the two optimization goals of code size and running speed is a key issue. Although the compil...

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Problems solved by technology

However, if the multi-objective optimization algorithm in the heuristic search algorithm is applied to the compilation and optimization sequence selection, since a large number of fitness evaluations are required for the population individuals in the process, the actual application needs to be compiled to obtain executable code. Obtaining the code size, another optimization goal in terms of running speed, calculating the fitness value also needs to actually run the program, which will bring about considerable computational cost constraints

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