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cs.DC2024
GraphPipe: Improving Performance and Scalability of DNN Training with Graph Pipeline Parallelism
Byungsoo Jeon, Mengdi Wu, Shiyi Cao +11
Deep neural networks (DNNs) continue to grow rapidly in size, making them infeasible to train on a single device. Pipeline parallelism is commonly used in existing DNN systems to s…
cs.LG2024★ 1 cited
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…
cs.DC2024
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…