Optimizing Deep Learning Workload Scheduling on Heterogeneous GPU Clusters

This ArXiv paper explores the optimization of deep learning workload scheduling within heterogeneous GPU clusters, likely leveraging hybrid learning and optimization techniques. The focus on dynamic scheduling suggests an attempt to improve resource utilization and reduce execution time for DL tasks.

• •Addresses the challenge of scheduling DL workloads on diverse GPU architectures.
• •Employs hybrid learning techniques, potentially combining machine learning with optimization methods.
• •Focuses on dynamic scheduling, aiming to adapt to changing workload demands and resource availability.