UltraEP: Unleash MoE Training and Inference on Rack-Scale Nodes with Near-Optimal Load Balancing
Researchers have developed UltraEP, a novel system designed to optimize the training and inference of large Mixture-of-Experts (MoE) models across rack-scale nodes. This system addresses the challenge of expert load imbalance, which can lead to performance bottlenecks and memory spikes. UltraEP achieves near-optimal load balancing by rebalancing experts on a microbatch and layer basis in real-time, significantly improving throughput and reducing imbalance compared to existing methods. AI
IMPACT Optimizes large-scale MoE model training and inference, potentially improving efficiency and reducing costs for AI operations.