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arXiv · 2605.21847

CompPow: A Case for Component-level GPU Power Management

Shaizeen Aga·Mohamed Assem Ibrahim
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Abstract

The ever increasing demand for ML-driven intelligence in a wide spectrum of domains has led to ubiquity of GPUs. At the same time, GPUs are notorious for their power consumption needs and often dominate power allocation in a typical ML datacenter. While datacenter-level power optimizations which focus on collection of GPUs are promising, in this work, we take a different tack -- namely, we take a closer look at power consumption inside a GPU. Specifically, as modern GPUs are comprised of integrated components, we make a case for component-awareness, termed CompPow in this work, for improved power management in modern GPUs. We demonstrate for a variety of ML operations and execution patterns, CompPow has the potential to deliver higher energy efficiency (10%) and even improved performance (5%). We conclude with recommendations on how component-aware software-hardware co-design can extract additional energy efficiency from modern GPUs.

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Shaizeen Aga, Mohamed Assem Ibrahim. 2026-05-21. CompPow: A Case for Component-level GPU Power Management. https://arxiv.org/abs/2605.21847

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