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Many organizations employ compute clusters equipped with accelerators such asGPUs and TPUs for training deep learning models in a distributed fashion.Training is resource-intensive, consuming significant compute, memory, andnetwork resources. Many prior works explore how to reduce training resourcefootprint without impacting quality, but their focus on a subset of thebottlenecks (typically only the network) limits their ability to improveoverall cluster utilization. In this work, we exploit the uniquecharacteristics of deep learning workloads to propose Structured PartialBackpropagation(SPB), a technique that systematically controls the amount ofbackpropagation at individual workers in distributed training. Thissimultaneously reduces network bandwidth, compute utilization, and memoryfootprint while preserving model quality. To efficiently leverage the benefitsof SPB at cluster level, we introduce JigSaw, a SPB aware scheduler, which doesscheduling at the iteration level for Deep Learning Training(DLT) jobs. We findthat JigSaw can improve large scale cluster efficiency by as high as 28\%.

Doing More by Doing Less: How Structured Partial Backpropagation Improves Deep Learning Clusters