  Deep neural networks (DNNs) have grown exponentially in size over the past
decade, leaving only those who have massive datacenter-based resources with the
ability to develop and train such models. One of the main challenges for the
long tail of researchers who might have only limited resources (e.g., a single
multi-GPU server) is limited GPU memory capacity compared to model size. The
problem is so acute that the memory requirement of training massive DNN models
can often exceed the aggregate capacity of all available GPUs on a single
server; this problem only gets worse with the trend of ever-growing model
sizes. Current solutions that rely on virtualizing GPU memory (by swapping
to/from CPU memory) incur excessive swapping overhead. In this paper, we
present a new training framework, Harmony, and advocate rethinking how DNN
frameworks schedule computation and move data to push the boundaries of
training massive models efficiently on a single commodity server. Across
various massive DNN models, Harmony is able to reduce swap load by up to two
orders of magnitude and obtain a training throughput speedup of up to 7.6x over
highly optimized baselines with virtualized memory.
