  The further development of deep neural networks is hampered by the limited
GPU memory resource. Therefore, the optimization of GPU memory resources is
highly demanded. Swapping and recomputation are commonly applied to make better
use of GPU memory in deep learning. However, as an emerging domain, several
challenges remain:1)The efficiency of recomputation is limited for both static
and dynamic methods. 2)Swapping requires offloading parameters manually, which
incurs a great time cost. 3) There is no such dynamic and fine-grained method
that involves tensor swapping together with tensor recomputation nowadays. To
remedy the above issues, we propose a novel scheduler manager named
DELTA(Dynamic tEnsor offLoad and recompuTAtion). To the best of our knowledge,
we are the first to make a reasonable dynamic runtime scheduler on the
combination of tensor swapping and tensor recomputation without user oversight.
In DELTA, we propose a filter algorithm to select the optimal tensors to be
released out of GPU memory and present a director algorithm to select a proper
action for each of these tensors. Furthermore, prefetching and overlapping are
deliberately considered to overcome the time cost caused by swapping and
recomputing tensors. Experimental results show that DELTA not only saves
40%-70% of GPU memory, surpassing the state-of-the-art method to a great extent
but also gets comparable convergence results as the baseline with acceptable
time delay. Also, DELTA gains 2.04$\times$ maximum batchsize when training
ResNet-50 and 2.25$\times$ when training ResNet-101 compared with the baseline.
Besides, comparisons between the swapping cost and recomputation cost in our
experiments demonstrate the importance of making a reasonable dynamic scheduler
on tensor swapping and tensor recomputation, which refutes the arguments in
some related work that swapping should be the first and best choice.
