Automatic Selection of Compiler Optimizations by Machine Learning

被引：0
|
作者：
Peker, Melih [1 ]
Ozturk, Ozcan [1 ]
Yildirim, Suleyman [2 ]
Ozturk, Mahiye Uluyagmur [2 ]
机构：
[1] Bilkent Univ, Bilgisayar Muhendisligi Bolumu, Bilkent, Turkiye
[2] Huawei Turkiye Ar Ge Merkezi, Istanbul, Turkiye
来源：
2023 31ST SIGNAL PROCESSING AND COMMUNICATIONS APPLICATIONS CONFERENCE, SIU | 2023年
关键词：
GCC; Compilers; Machine Learning; Optimization;
D O I：
10.1109/SIU59756.2023.10223902
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Many widely used telecommunications applications have extremely long run times. Therefore, faster and more efficient execution of these codes on the same hardware is important in critical telecommunication applications such as base stations. Compilers greatly affect the properties of the executable program to be created. It is possible to change properties such as compilation speed, execution time, power consumption and code size using compiler flags. This study aims to find the set of flags that will provide the shortest run time among hundreds of compiler flag combinations in GCC using code flow analysis, loop analysis and machine learning methods without running the program.
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页数：4
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