We introduce Gandiva, a new cluster scheduling framework that utilizes domain-specific knowledge to improve latency and efficiency of training deep learning models in a GPU cluster. One key characteristic of deep learning is feedback-driven exploration, where a user often runs a set of jobs (or a multi-job) to achieve the best result for a specific mission and uses early feedback on accuracy to dynamically prioritize or kill a subset of jobs; simultaneous early feedback on the entire multi-job is critical. A second characteristic is the heterogeneity of deep learning jobs in terms of resource usage, making it hard to achieve best-fit a priori. Gandiva addresses these two challenges by exploiting a third key characteristic of deep learning: intra-job predictability, as they perform numerous repetitive iterations called mini-batch iterations. Gandiva exploits intra-job predictability to time-slice GPUs efficiently across multiple jobs, thereby delivering low-latency. This predictability is also used for introspecting job performance and dynamically migrating jobs to better-fit GPUs, thereby improving cluster efficiency. We show via a prototype implementation and micro-benchmarks that Gandiva can speed up hyper-parameter searches during deep learning by up to an order of magnitude, and achieves better utilization by transparently migrating and time-slicing jobs to achieve better job-to-resource fit. We also show that, in a real workload of jobs running in a 180-GPU cluster, Gandiva improves aggregate cluster utilization by 26%, pointing to a new way of managing large GPU clusters for deep learning.<\/p>\n","protected":false},"excerpt":{"rendered":"
We introduce Gandiva, a new cluster scheduling framework that utilizes domain-specific knowledge to improve latency and efficiency of training deep learning models in a GPU cluster. One key characteristic of deep learning is feedback-driven exploration, where a user often runs a set of jobs (or a multi-job) to achieve the best result for a specific 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