Themis: Fair and Efficient GPU Cluster Scheduling
The paper introduces Themis, a GPU cluster scheduling framework geared towards optimizing fairness and efficiency for ML workloads. In contemporary ML environments, GPUs are critical resources due to their ability to handle high-dimensional data required for training complex models. However, contention issues arise when multiple ML workloads share a clustered GPU environment. Themis was developed to address this contention, with a focus on ensuring fairness in GPU allocation—a critical concern for both users and operators.
Themis redefines fairness through the novel concept of finish-time fairness, which ensures that workloads complete equitably in shared environments compared to dedicated clusters. A distinct characteristic of Themis is its two-level scheduling architecture: this includes an auction-based GPU allocation system overseen by a central arbiter. This structure contrasts with conventional systems like DRF and Tiresias by acknowledging the interplay between resource allocation and placement, thereby enabling better scheduling for long-duration tasks and placement-sensitive workloads.
The evaluation of Themis on realistic traces shows compelling improvements in fairness and efficiency. Themis is shown to enhance fairness by over 2.25 times compared to existing state-of-the-art schedulers, displaying a 5–250% increase in cluster efficiency. These results highlight Themis’s advanced capability to provide a fairer allocation of resources without compromising on cluster throughput or job completion time.
Implications and Future Directions
Practical Implications: Themis's introduction of finish-time fairness and its auction-based resource allocation have profound implications for ML scheduling in shared environments. By offering equitable resource distribution, Themis provides a concrete solution to the prevalent problem of resource hoarding, thus minimizing user frustration and potentially reducing the need for dedicated hardware setups. Moreover, its approach to bidding allows for more dynamic and fine-tuned resource allocation, accommodating variations in workload requirements over time.
Theoretical Implications: Themis challenges the dominance of rigid scheduling systems, presenting a flexible method that accounts for the nuanced demands of ML workflows. Its model foresees scenarios where ML workloads exhibit diverse placement preferences, indicating a need for deeper investigation into how internal job characteristics influence allocation success within shared GPU clusters.
Speculations on AI Development: As AI technologies evolve, Themis’s bidding mechanism can be adapted to more complex environments where resource management extends beyond GPU clusters to hybrid cloud setups. The integration of advanced data analytics could further refine bidding strategies, allowing Themis to make allocation decisions based on predictive modeling of workload characteristics.
In conclusion, Themis represents a significant step forward in GPU cluster scheduling for ML workloads, offering a robust framework that balances efficiency with equitable resource distribution—a necessity as pressure on computational resources escalates amidst the rise of sophisticated AI models. Further research could focus on extending Themis’s scheduling algorithms to handle integrated data flow across heterogeneous computational resources, fortifying its place in next-generation AI infrastructure management.