Dike: Enhancing Fairness and Efficiency in GPU Clusters for Deep Learning

The advent of deep learning (DL) has transformed signal interpretation, enabling more efficient solutions to complex signal processing problems. DL workloads in signal processing typically share the computational resources of GPU clusters. However, the unpredictable nature of the duration of the DL job introduces a dynamic environment in these clusters. Existing GPU cluster schedulers rely heavily on prior knowledge, overlooking the system’s evolving dynamism. Balancing individual job quality of service (QoS) with overall cluster efficiency is critical from a fairness perspective. Unfortunately, most current schedulers prioritize minimizing job completion time (JCT) at the expense of fairness. This paper introduces Dike, an information-agnostic framework. We propose discrete mixed-priority feedback queues and a communication-aware job placement strategy to balance fairness and efficiency. Large-scale trace-driven experiments demonstrate that Dike improves cluster efficiency by over 1.65 × compared to state-of-the-art resource schedulers.