A systematic evaluation of GPU memory and utilization estimators across analytical, library-based, and ML paradigms identifies key limitations in generalization, integration overhead, and hardware variability for training-aware resource management.
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The paper reviews energy-aware computing literature and constructs a taxonomy organized by hardware/software aspects, measurement, optimizations, scheduling, scaling, consolidation, federated learning, and cooling.
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GPU Memory and Utilization Estimation for Training-Aware Resource Management: Opportunities and Limitations
A systematic evaluation of GPU memory and utilization estimators across analytical, library-based, and ML paradigms identifies key limitations in generalization, integration overhead, and hardware variability for training-aware resource management.
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Energy-Aware Computing in the Year 2026
The paper reviews energy-aware computing literature and constructs a taxonomy organized by hardware/software aspects, measurement, optimizations, scheduling, scaling, consolidation, federated learning, and cooling.