A principled approach for energy-efficient training via phase-aware GPU frequency tuning
Read the original on arXiv Machine Learning →The Flow has not summarised this story yet — read it at arXiv Machine Learning.
The Flow has not summarised this story yet — read it at arXiv Machine Learning.
The paper proposes a simple technique of chunking workloads into smaller parts that alternate between compute-intensive and memory-bound operations to smooth power and temperature spikes in GPU systems. By doing so, it prevents throttling, leading to faster wall-clock times and lower total energy consumption. Experiments on a DGX Spark show up to 2% performance and energy gains, while similar benefits, though smaller, are observed on multi‑GPU servers.
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