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Arena: Efficiently Training Large Models via Dynamic Scheduling and Adaptive Parallelism Co-Design
Chunyu Xue, Weihao Cui, Quan Chen +10
Efficiently training large-scale models (LMs) in GPU clusters involves two separate avenues: inter-job dynamic scheduling and intra-job adaptive parallelism (AP). However, existing…
MuxTune: Efficient Multi-Task LLM Fine-Tuning in Multi-Tenant Datacenters via Spatial-Temporal Backbone Multiplexing
Chunyu Xue, Yi Pan, Weihao Cui +4
Parameter-Efficient Fine-Tuning (PEFT) is widely applied as the backend of fine-tuning APIs for large language model (LLM) customization in datacenters. Service providers deploy se…
Harli: SLO-Aware Co-location of LLM Inference and PEFT-based Finetuning on Model-as-a-Service Platforms
Ao Xu, Han Zhao, Weihao Cui +7
Large language models (LLMs) are increasingly deployed under the Model-as-a-Service (MaaS) paradigm. To meet stringent quality-of-service (QoS) requirements, existing LLM serving s…
VQ-LLM: High-performance Code Generation for Vector Quantization Augmented LLM Inference
Zihan Liu, Xinhao Luo, Junxian Guo +11
In this work, we design and implement VQ-LLM, an efficient fused Vector Quantization (VQ) kernel generation framework. We first introduce a software abstraction called codebook cac…
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…
Vortex: Efficient Sample-Free Dynamic Tensor Program Optimization via Hardware-aware Strategy Space Hierarchization
Yangjie Zhou, Honglin Zhu, Qian Qiu +9
Dynamic-shape deep neural networks (DNNs) are rapidly evolving, attracting attention for their ability to handle variable input sizes in real-time applications. However, existing c…