SMART-MIG applies MF-MARL for constant-complexity MIG repartitioning plus heuristics for scheduling, reporting 18% better energy-tardiness efficiency than static partitioning and 27% above a theoretical energy lower bound.
Energy- efficient gpu clusters scheduling for deep learning
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CompPow makes the case that component-aware power management inside GPUs can yield 10% higher energy efficiency and 5% better performance for ML workloads.
A hierarchical review of energy storage technologies for smoothing the sub-second variable loads of AI data centers on the utility grid.
citing papers explorer
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SMART-MIG: A Learning Framework for Scalable and Energy-Efficient GPU Scheduling
SMART-MIG applies MF-MARL for constant-complexity MIG repartitioning plus heuristics for scheduling, reporting 18% better energy-tardiness efficiency than static partitioning and 27% above a theoretical energy lower bound.
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CompPow: A Case for Component-level GPU Power Management
CompPow makes the case that component-aware power management inside GPUs can yield 10% higher energy efficiency and 5% better performance for ML workloads.
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Grid Integration of AI Data Centers: A Critical Review of Energy Storage Solutions
A hierarchical review of energy storage technologies for smoothing the sub-second variable loads of AI data centers on the utility grid.