Low-latency job scheduling with preemption for the development of deep learning

5 Feb 2019Hidehito YabuuchiDaisuke TaniwakiShingo Omura

One significant challenge in the job scheduling of computing clusters for the development of deep learning algorithms is the efficient scheduling of trial-and-error (TE) job, the type of job in which the users seek to conduct small-scale experiments while monitoring their processes. Unfortunately, the existing job schedulers to date do not feature well-balanced scheduling for the mixture of TE jobs and best-effort (BE) jobs, or they can handle the mixture in limited situations at most... (read more)

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