Device Placement

Device placement in distributed machine learning focuses on optimizing the allocation of computational tasks across multiple devices to minimize training or inference time. Current research emphasizes developing efficient algorithms, often employing integer linear programming, reinforcement learning, or transformer-based architectures, to determine optimal placements considering factors like network topology, hardware heterogeneity, and model sparsity. These advancements aim to significantly improve the scalability and efficiency of training and deploying large-scale machine learning models, impacting both research productivity and real-world applications. The ultimate goal is to achieve near-optimal performance across diverse hardware configurations and model architectures.