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Optimal Resource Efficiency with Fairness in Heterogeneous GPU Clusters


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."

Deeper Inquiries

How can OEF handle scenarios where users have varying priorities for their jobs

OEF can handle scenarios where users have varying priorities for their jobs by implementing a weighted allocation mechanism. In this approach, each user is assigned a weight that represents their level of importance or priority. Users with higher weights are allocated more resources to reflect their priority level. This weighted allocation ensures that users with different priorities receive an allocation that aligns with their importance. Additionally, OEF replicates the speedup vector for users with higher weights to adjust the allocation accordingly. By dividing the weight among virtual users representing different job types, OEF can accommodate users running multiple types of jobs simultaneously while maintaining fairness and efficiency.

What are the potential drawbacks of prioritizing fairness over resource efficiency in heterogeneous GPU clusters

The potential drawbacks of prioritizing fairness over resource efficiency in heterogeneous GPU clusters include reduced overall training throughput and suboptimal resource utilization. When fairness is prioritized at the expense of efficiency, users with slower speedups may receive an unfair advantage, leading to inefficient resource allocation. This can result in lower overall training throughput and decreased resource efficiency within the cluster. Additionally, focusing solely on fairness may lead to conflicts between different fairness properties, such as envy-freeness and sharing-incentive, which can further impact the overall performance of the system.

How can the principles of OEF be applied to other resource allocation systems beyond GPU clusters

The principles of OEF can be applied to other resource allocation systems beyond GPU clusters by adapting the framework to suit the specific requirements of different environments. OEF's approach of integrating resource efficiency and fairness within a global optimization framework can be extended to various multi-tenant systems where users compete for shared resources. By customizing the allocation mechanisms and constraints based on the characteristics of the system, OEF's principles can be implemented in cloud computing platforms, distributed computing systems, and other resource-sharing environments. The key lies in designing allocation schemes that balance efficiency, fairness, and user priorities to optimize resource utilization and overall system performance.
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