Combining Machine Learning with a Genetic Algorithm to Find Good Complier Optimizations Sequences

被引：2
作者：
Queiroz Junior, Nilton Luiz [1 ]
Araujo Rodriguez, Luis Gustavo [1 ]
da Silva, Anderson Faustino [1 ]
机构：
[1] Univ Estadual Maringa, Dept Informat, Maringa, Parana, Brazil
来源：
ICEIS: PROCEEDINGS OF THE 19TH INTERNATIONAL CONFERENCE ON ENTERPRISE INFORMATION SYSTEMS - VOL 1 | 2017年
关键词：
Optimization Selection Problem; Machine Learning; Genetic Algorithms;
D O I：
10.5220/0006270403970404
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
Artificial Intelligence is a strategy applied in several problems in computer science. One of them is to find good compilers optimizations sequences for programs. Currently, strategies such as Genetic Algorithms and Machine Learning have been used to solve it. This article propose an approach that combines both, Machine Learning and Genetic Algorithms, to solve this problem. The obtained results indicate that the proposed approach achieves performance up to 3.472% over Genetic Algorithms and 4.94% over Machine Learning.
引用
收藏
页码：397 / 404
页数：8
相关论文
共 18 条
  • [1] [Anonymous], 2016, LLVM COMPILER INFRAS
  • [2] [Anonymous], 2013, 2013 International Conference on Compilers, Architecture and Synthesis for Embedded Systems (CASES)
  • [3] [Anonymous], 2006, COMPILERS PRINCIPLES
  • [4] A Comparative Study of Three Artificial Intelligence Techniques: Genetic Algorithm, Neural Network, and Fuzzy Logic, on Scheduling Problem
    Ansari, Abdollah
    Abu Bakar, Azuraliza
    [J]. PROCEEDINGS 2014 4TH INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE WITH APPLICATIONS IN ENGINEERING AND TECHNOLOGY ICAIET 2014, 2014, : 31 - 36
  • [5] Evaluating Iterative Optimization Across 1000 Data Sets
    Chen, Yang
    Huang, Yuanjie
    Eeckhout, Lieven
    Fursin, Grigori
    Peng, Liang
    Temam, Olivier
    Wu, Chengyong
    [J]. PLDI '10: PROCEEDINGS OF THE 2010 ACM SIGPLAN CONFERENCE ON PROGRAMMING LANGUAGE DESIGN AND IMPLEMENTATION, 2010, : 448 - 459
  • [6] de Lima ED, 2013, INT WORKS POW TIM, P142, DOI 10.1109/PATMOS.2013.6662167
  • [7] Performance Potential of Optimization Phase Selection During Dynamic JIT Compilation
    Jantz, Michael R.
    Kulkarni, Prasad A.
    [J]. ACM SIGPLAN NOTICES, 2013, 48 (07) : 131 - 141
  • [8] Malik A. M., 2010, 2010 Ninth International Conference on Machine Learning and Applications (ICMLA 2010), P925, DOI 10.1109/ICMLA.2010.147
  • [9] Clustering-Based Selection for the Exploration of Compiler Optimization Sequences
    Martins, Luiz G. A.
    Nobre, Ricardo
    Cardoso, Joao M. P.
    Delbem, Alexandre C. B.
    Marques, Eduardo
    [J]. ACM TRANSACTIONS ON ARCHITECTURE AND CODE OPTIMIZATION, 2016, 13 (01)
  • [10] Practical Aggregation of Semantical Program Properties for Machine Learning Based Optimization
    Namolaru, Mircea
    Cohen, Albert
    Fursin, Grigori
    Zaks, Ayal
    Freund, Ari
    [J]. PROCEEDINGS OF THE 2010 INTERNATIONAL CONFERENCE ON COMPILERS, ARCHITECTURES AND SYNTHESIS FOR EMBEDDED SYSTEMS (CASES '10), 2010, : 197 - 206
12