Compiler Optimization Prediction with New Self-Improved Optimization Model

Abstract

Users may now choose from a vast range of compiler optimizations. These optimizations interact in a variety of sophisticated ways with one another and with the source code. The order in which optimization steps are applied can have a considerable influence on the performance obtained. As a result, we created a revolutionary compiler optimization prediction model. Our model comprises three operational phases: model training, feature extraction, as well as model exploitation. The model training step includes initialization as well as the formation of candidate sample sets. The inputs were then sent to the feature extraction phase, which retrieved static, dynamic, and improved entropy features. These extracted features were then optimized by the feature exploitation phase, which employs an improved hunger games search algorithm to choose the best features. In this work, we used a Convolutional Neural Network to predict compiler optimization based on these selected characteristics, and the findings show that our innovative compiler optimization model surpasses previous approaches.

Artificial intelligence approach for terror attacks prediction through machine learning

“…One of the most common issues that researchers encounter when it comes to developing machine learning systems is overfitting (Khoje and Shinde 2023). This occurs when the training model starts to learn the underlying relationships and patterns instead of learning the details of the training (Shewale et al 2023). Two of the techniques that are commonly used to address this issue are LDA and PCA.…”