  First-come first-serve scheduling can result in substantial (up to 10%) of
transiently idle nodes on supercomputers. Recognizing that such unfilled nodes
are well-suited for deep neural network (DNN) training, due to the flexible
nature of DNN training tasks, Liu et al. proposed that the re-scaling DNN
training tasks to fit gaps in schedules be formulated as a mixed-integer linear
programming (MILP) problem, and demonstrated via simulation the potential
benefits of the approach. Here, we introduce MalleTrain, a system that provides
the first practical implementation of this approach and that furthermore
generalizes it by allowing it use even for DNN training applications for which
model information is unknown before runtime. Key to this latter innovation is
the use of a lightweight online job profiling advisor (JPA) to collect critical
scalability information for DNN jobs -- information that it then employs to
optimize resource allocations dynamically, in real time. We describe the
MalleTrain architecture and present the results of a detailed experimental
evaluation on a supercomputer GPU cluster and several representative DNN
training workloads, including neural architecture search and hyperparameter
optimization. Our results not only confirm the practical feasibility of
leveraging idle supercomputer nodes for DNN training but improve significantly
on prior results, improving training throughput by up to 22.3\% without
requiring users to provide job scalability information.
