6 citations · 12 across the 8 of their papers we have counts for
4 papers · 1 filter
BladeDISC++: Memory Optimizations Based On Symbolic Shape
Xiulong Yuan, Xu Yan, Wenting Shen +5
Recent deep learning workloads exhibit dynamic characteristics, leading to the rising adoption of dynamic shape compilers. These compilers can generate efficient kernels for dynami…
Rubick: Exploiting Job Reconfigurability for Deep Learning Cluster Scheduling
Xinyi Zhang, Hanyu Zhao, Wencong Xiao +5
The era of large deep learning models has given rise to advanced training strategies such as 3D parallelism and the ZeRO series. These strategies enable various (re-)configurable e…
Llumnix: Dynamic Scheduling for Large Language Model Serving
Biao Sun, Ziming Huang, Hanyu Zhao +4
Inference serving for large language models (LLMs) is the key to unleashing their potential in people's daily lives. However, efficient LLM serving remains challenging today becaus…
Infinite-LLM: Efficient LLM Service for Long Context with DistAttention and Distributed KVCache
Bin Lin, Chen Zhang, Tao Peng +12
Large Language Models (LLMs) demonstrate substantial potential across a diverse array of domains via request serving. However, as trends continue to push for expanding context size…