5 citations · 7 across the 10 of their papers we have counts for
7 papers · 1 filter
Charon: A Unified and Fine-Grained Simulator for Large-Scale LLM Training and Inference
Mengtian Yang, Zhekun Zhang, Mingheng Wu +3
Deploying large-scale LLM training and inference with optimal performance is exceptionally challenging due to a complex design space of parallelism strategies, system optimizations…
UniEP: Unified Expert-Parallel MoE MegaKernel for LLM Training
Size Zheng, Xuegui Zheng, Li-wen Chang +1
The exponential growth in Large Language Model (LLM) parameters has transformed model training into an increasingly resource-intensive endeavor. With the stagnation of Moore's Law…
Optimizing Long-context LLM Serving via Fine-grained Sequence Parallelism
Cong Li, Yuzhe Yang, Xuegui Zheng +7
With the advancement of large language models (LLMs), their context windows have rapidly expanded. To meet diverse demands from varying-length requests in online services, existing…
TileLink: Generating Efficient Compute-Communication Overlapping Kernels using Tile-Centric Primitives
Size Zheng, Jin Fang, Xuegui Zheng +9
Large deep learning models have achieved state-of-the-art performance in a wide range of tasks. These models often necessitate distributed systems for efficient training and infere…
Triton-distributed: Programming Overlapping Kernels on Distributed AI Systems with the Triton Compiler
Size Zheng, Wenlei Bao, Qi Hou +19
In this report, we propose Triton-distributed, an extension of existing Triton compiler, to overcome the programming challenges in distributed AI systems. Triton-distributed is the…
MegaScale-Infer: Serving Mixture-of-Experts at Scale with Disaggregated Expert Parallelism
Ruidong Zhu, Ziheng Jiang, Chao Jin +17
Mixture-of-Experts (MoE) showcases tremendous potential to scale large language models (LLMs) with enhanced performance and reduced computational complexity. However, its sparsely…
Comet: Fine-grained Computation-communication Overlapping for Mixture-of-Experts
Shulai Zhang, Ningxin Zheng, Haibin Lin +9
Mixture-of-experts (MoE) has been extensively employed to scale large language models to trillion-plus parameters while maintaining a fixed computational cost. The development of l…