An Efficient Optimization Method for Computational Cost Constrained Problems in Compiler Optimization

Abstract

The invention provides an efficient optimization method oriented to the calculation cost constraint problem in compilation optimization, is a solution to the calculation cost constraint problem, and belongs to the field of compiler optimization. In this method, the compiled optimization sequence is first binary coded and the fitness function is designed, and a new population is iteratively generated by crossover operation and selection operation, so as to obtain the optimal solution. In this iterative process, an approximate surrogate function is constructed using the compiled optimization sequence and the corresponding fitness value, a local search is performed for each individual in the population, and the individual is replaced with a local optimal solution, thereby improving evolutionary efficiency. The invention can effectively select the optimal compilation optimization sequence for the program to be compiled, accelerate the iteration speed, and improve the operation efficiency of the algorithm.

Description

technical field [0001] The invention belongs to the technical field of compiler optimization, and is a solution to the calculation cost constraint problem in the compilation optimization sequence selection. Background technique [0002] Compiler is a very important basic tool in the process of software development. It takes the source program written in high-level computer language as input, and after lexical analysis, syntax analysis, semantic analysis, intermediate code generation, code optimization, and object code generation, it finally obtains Executable target machine code. In the actual development process, the compiled target machine code needs to have relatively ideal performance, such as small executable code size, fast execution speed, and low power consumption. Therefore, the compiler provides a large number of compilation optimization options, and the developer needs to select a set of compilation optimization options according to the actual situation to form a...

AI Technical Summary

Problems solved by technology

However, this solution suffers from computational cost constraints

For example, in the genetic algorithm, the optimization effect of each compilation optimization sequence in the population needs to be calculated in each round of iteration, that is, the compiler needs to use each optimization sequence to compile the program to be compiled once, and execute the program to obtain the optimization effect, which The execution time overhead of the algorithm is very huge, which brings about the problem of computational cost constraints

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