SwitchFlow
Xiaofeng Wu, Jia Rao, Wei Chen, Hang Huang, Chris H. Q. Ding, Heng Huang · 2021
Accelerators, such as GPU, are a scarce resource in deep learning (DL). Effectively and efficiently sharing GPU leads to improved hardware utilization as well as user experiences, who may need to wait for hours to access GPU before a long training job is done. Spatial and temporal multitasking on GPU have been studied in the literature, but popular deep learning frameworks, such as Tensor-Flow and PyTorch, lack the support of GPU sharing among multiple DL models, which are typically represented as computation graphs, heavily optimized by underlying DL libraries, and run on a complex pipeline spanning CPU and GPU. Our study shows that GPU kernels, spawned from computation graphs, can barely execute simultaneously on a single GPU and time slicing may lead to low GPU utilization.