Papers › Computron: Serving Distributed Deep Learning Models with Model Parallel Swapping
Computron: Serving Distributed Deep Learning Models with Model Parallel Swapping
Daniel Zou, Xinchen Jin, Xueyang Yu, Hao Zhang, James Demmel
Many of the most performant deep learning models today in fields like language and image understanding are fine-tuned models that contain billions of parameters. In anticipation of workloads that involve serving many of such large models to handle different tasks, we develop Computron, a system that uses memory swapping to serve multiple distributed models on a shared GPU cluster. Computron implements a model parallel swapping design that takes advantage of the aggregate CPU-GPU link bandwidth of a cluster to speed up model parameter transfers. This design makes swapping large models feasible and can improve resource utilization. We demonstrate that Computron successfully parallelizes model swapping on multiple GPUs, and we test it on randomized workloads to show how it can tolerate real world variability factors like burstiness and skewed request rates. Computron's source code is available at https://github.com/dlzou/computron.
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