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Algorithmic Techniques for GPU Scheduling: A Comprehensive Survey

I tiakina i:
I whakaputaina i:Algorithms vol. 18, no. 7 (2025), p. 385-437
Kaituhi matua: Chab, Robert
Ētahi atu kaituhi: Li, Fei, Setia Sanjeev
I whakaputaina:
MDPI AG
Ngā marau:
NVidia Corp
Scheduling
Machine learning
Dynamic programming
Task scheduling
Deep learning
Performance evaluation
Mathematical analysis
Artificial intelligence
Formal method
Graphics processing units
Bandwidths
Mathematical programming
Greedy algorithms
Architecture
Energy efficiency
Spectrum allocation
Algorithms
Resource management
Cost control
Workloads
Decisions
Queueing
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Whakarāpopotonga: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. The analysis shows that no one paradigm dominates; instead, the highest-performing schedulers blend the predictability of formal methods with the adaptability of learning, often moderated by queueing insights for fairness. We also discuss key challenges in optimizing GPU resource management and suggest potential solutions.
ISSN:1999-4893
DOI:10.3390/a18070385
Puna:Engineering Database