5 papers
MPK: A Compiler and Runtime for Mega-Kernelizing Tensor Programs
Xinhao Cheng, Zhihao Zhang, Yu Zhou +17
We introduce Mirage Persistent Kernel (MPK), the first compiler and runtime system that automatically transforms multi-GPU model inference into a single high-performance mega-kerne…
On the Trustworthiness of Generative Foundation Models: Guideline, Assessment, and Perspective
Yue Huang, Chujie Gao, Siyuan Wu +63
Generative Foundation Models (GenFMs) have emerged as transformative tools. However, their widespread adoption raises critical concerns regarding trustworthiness across dimensions.…
FlexLLM: Token-Level Co-Serving of LLM Inference and Finetuning with SLO Guarantees
Gabriele Oliaro, Xupeng Miao, Xinhao Cheng +9
Finetuning large language models (LLMs) is essential for task adaptation, yet today's serving stacks isolate inference and finetuning on separate GPU clusters -- wasting resources…
SuffixDecoding: Extreme Speculative Decoding for Emerging AI Applications
Gabriele Oliaro, Zhihao Jia, Daniel Campos +1
Speculative decoding is widely adopted to reduce latency in large language model (LLM) inference by leveraging smaller draft models capable of handling diverse user tasks. However,…
Mirage: A Multi-Level Superoptimizer for Tensor Programs
Mengdi Wu, Xinhao Cheng, Shengyu Liu +7
We introduce Mirage, the first multi-level superoptimizer for tensor programs. A key idea in Mirage is Graphs, a uniform representation of tensor programs at the kernel, thread…