arXiv Machine Learning By Miguel Braga, J\'ulio Pinto, Rahma Nouaji, Olivier Michaud, Bettina Kemme, Oana Balmau, Cl\'audia Brito, Ricardo Macedo

A principled approach for energy-efficient training via phase-aware GPU frequency tuning

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arXiv AI
Oct 26

Power- and Fragmentation-aware Online Scheduling for GPU Datacenters

The paper introduces PWR, a scheduling policy that selects power‑efficient GPU and CPU combinations to reduce power consumption in GPU datacenters. It complements the existing Fragmentation Gradient Descent (FGD) policy, which mitigates GPU fragmentation by using a fragmentation metric. Experiments in a simulated cluster show that combining PWR with FGD balances power savings and fragmentation reduction.

By Francesco Lettich, Emanuele Carlini, Franco Maria Nardini, Raffaele Perego, Salvatore Trani
arXiv Machine Learning
Sep 14

One Simple Trick for Improving the Performance of Energy-Limited Local Inference and Training

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.

By Erik Schultheis, Maximilian Kleinegger, Dan Alistarh
arXiv AI
Oct 31

The Illusion of Power Capping in LLM Decode: A Phase-Aware Energy Characterisation Across Attention Architectures

The paper investigates the effectiveness of GPU power capping during large language model (LLM) autoregressive decoding. It finds that power caps have minimal impact on decoding performance because decode kernels are memory‑bound and the GPU idles between launches, drawing less than half its 700 W rating. By instead locking the SM clock, the authors can reduce power consumption by up to 32 % with negligible throughput loss and identify three architecture‑dependent frequency‑scaling classes that influence energy use.

By Bole Ma, Ayesha Afzal, Jan Eitzinger, Gerhard Wellein