Machine Learning Based Compiler Optimization Technique

Salwa Iqbal, Mr.Kashif Sheikh
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Abstract

Since the last decade to upcoming era the need of machine learning based compilation approaches has become indispensable for every aspect of growing technology especially artificial intelligence and network computing areas. These approaches are used to enhance the result quality by their improved performance as well as handles compiler optimization problems namely optimization selection and phase ordering. It has enthused from a vague research place in middle of the road movement. Wealth of present compiler optimization techniques leads to finding the best heuristic parameters to tune each optimization tactic using machine learning. In this research paper, we highlighted the term machine learning and compiler along with relationship between compiler optimization and machine learning with the identity of the concept of models, training, and approaches.
基于机器学习的编译器优化技术
从过去十年到即将到来的新时代,基于机器学习的编译方法已成为不断发展的技术,尤其是人工智能和网络计算领域不可或缺的各个方面。这些方法通过改善性能来提高结果质量,并处理编译器优化问题,即优化选择和阶段排序。编译器优化技术已经从模糊的研究领域跃升为中坚力量。目前编译器优化技术的丰富之处在于利用机器学习找到最佳启发式参数来调整每种优化策略。在这篇研究论文中,我们强调了机器学习和编译器这一术语,以及编译器优化和机器学习之间的关系,以及模型、训练和方法等概念的特性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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