Waterwave: A GPU Memory Flow Engine for Concurrent DNN Training

Training Deep Neural Networks (DNN) concurrently is becoming increasingly important for deep learning practitioners, e.g.,
hyperparameter optimization (HPO)
and
neural architecture search (NAS)
. The GPU memory capacity is the impediment that prohibits multiple DNNs from being trained on the same GPU due to the large memory usage during training. In this paper, we propose
Waterwave
, a GPU memory flow engine for concurrent deep learning training. First, to address the memory explosion brought by the long time lag between memory allocation and deallocation time, we develop an allocator tailored for multi-streams. By making the allocator aware of the stream information, a
prioritized allocation
is conducted based on the chunk's
synchronization
attributes, allowing us to provide useable memory after scheduling rather than waiting it to be really released after GPU computation. Second,
Waterwave
partitions the compute graph to a set of continuous
node groups
and then performs finer-grained scheduling:
NodeGroup pipeline execution
, to guarantee a proper memory requests order.
Waterwave
can accomplish up to 96.8% of the maximum batch size of solo training. Additionally, in scenarios with high memory demand,
Waterwave
can outperform existing spatial sharing and temporal sharing by up to 12x and 1.49x, respectively.