Algorithmic Techniques for GPU Scheduling: A Comprehensive Survey
محفوظ في:
| الحاوية / القاعدة: | Algorithms vol. 18, no. 7 (2025), p. 385-437 |
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| المؤلف الرئيسي: | |
| مؤلفون آخرون: | , |
| منشور في: |
MDPI AG
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| الموضوعات: | |
| الوصول للمادة أونلاين: | Citation/Abstract Full Text + Graphics Full Text - PDF |
| الوسوم: |
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| مستخلص: | 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. |
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| تدمد: | 1999-4893 |
| DOI: | 10.3390/a18070385 |
| المصدر: | Engineering Database |