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cs.DC2025
Efficient MoE Inference with Fine-Grained Scheduling of Disaggregated Expert Parallelism
Xinglin Pan, Shaohuai Shi, Wenxiang Lin +4
The mixture-of-experts (MoE) architecture scales model size with sublinear computational increase but suffers from memory-intensive inference due to KV caches and sparse expert act…
cs.DC2025
ElasWave: An Elastic-Native System for Scalable Hybrid-Parallel Training
Xueze Kang, Guangyu Xiang, Yuxin Wang +16
Large-scale LLM pretraining now runs across -- accelerators, making failures routine and elasticity mandatory. We posit that an elastic-native training system must join…
cs.DC2025
DreamDDP: Accelerating Data Parallel Distributed LLM Training with Layer-wise Scheduled Partial Synchronization
Zhenheng Tang, Zichen Tang, Junlin Huang +7
The growth of large language models (LLMs) increases challenges of accelerating distributed training across multiple GPUs in different data centers. Moreover, concerns about data p…