Algorithmic Techniques for GPU Scheduling: A Comprehensive Survey

In this survey, we provide a comprehensive classification of GPU task scheduling approaches, categorized by their underlying algorithmic techniques and evaluation metrics. We examine traditional methods --- including greedy algorithms, dynamic programming, and mathematical programming --- alongside advanced machine learning techniques integrated into scheduling policies. We also evaluate the performance of these approaches across diverse applications. This work focuses on understanding the trade-offs among various algorithmic techniques, the architectural and job-level factors influencing scheduling decisions, and the balance between user-level and service-level objectives. Finally, we discuss key challenges in optimizing GPU resource management and suggest potential solutions.