Core Concepts
Introducing OEF for optimal resource efficiency and fairness in heterogeneous GPU clusters.
Abstract
The content discusses the challenges of balancing resource efficiency and fairness in heterogeneous GPU clusters. It introduces OEF, a new resource allocation framework, to address these challenges. OEF aims to maximize resource efficiency while ensuring fairness properties in both cooperative and non-cooperative environments. The content outlines the design challenges, architecture, workflow, allocation mechanisms, placement optimization, and implementation details of OEF. It also highlights the properties of OEF, including pareto-efficiency, strategy-proofness, optimal resource efficiency, sharing-incentive, and envy-freeness.
Stats
OEF ensures sharing-incentive and envy-freeness in cooperative environments.
OEF achieves strategy-proofness in non-cooperative environments.
Quotes
"Ensuring the highest training throughput to maximize resource efficiency, while maintaining fairness among users, is critical for deep learning (DL) training in heterogeneous GPU clusters."
"OEF is capable of providing users with maximized overall efficiency, as well as various guarantees of fairness, in both cooperative and non-cooperative environments."