Application of Machine Learning to GPU Optimization, Deep Q-Networks and Computational Fluid Dynamics

Abstract

Throughout society today, machine learning has been catapulted to a transformative problem solving approach across various domains, ranging from natural language processing to computer vision to engineering optimization. The fundamental principle is the ability of algorithms to learn patterns and make decisions based on data, rather than relying on explicitly programmed instructions. This dissertation addresses the research question: “How can machine learning techniques be applied to improve computational efficiency and prediction accuracy in high-performance scientific computing tasks, including GPU kernel optimization, Deep Q-Networks, and computational fluid dynamics?” To answer the question, we devised three distinct problems, each of which is orthogonal to the next to represent a wide breadth of exploration. The problems focus on the two paradigms of supervised learning and reinforcement learning.