Year: 2024
Type: conference-paper
Abstract: Training large AI models on numerous GPUs consumes a massive amount of energy, making power delivery one of the largest limiting factors in building and operating datacenters for AI workloads. However, we observe that not all energy consumed during training directly contributes to end-to-end throughput; a significant portion can be removed without slowing down training. We call this portion energy bloat.
Language: English
Subfield: Hardware and Architecture
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Awards: Collaborative Research: AF: Small: New Directions and Approaches in Discrepancy Theory, Collaborative Research: CNS Core: Medium: Systems Support for Federated Learning, Continuous Methods in Discrete Optimization, Collaborative Research: NGSDI: Foundations of Clean and Balanced Datacenters: Treehouse
OpenAlex ID: W4404400711