# CrossoverScheduler: Overlapping Multiple Distributed Training Applications in a Crossover Manner Source: [CrossoverScheduler: Overlapping Multiple Distributed Training Applications in a Crossover Manner]() Collection index: [llms.txt]() Data updated: 2025-10-25T23:01:46.798Z Authors: Cheng Luo, Lei Qu, Youshan Miao, Peng Cheng, Yongqiang Xiong Published: 2021-03-14T17:01:15.000Z ## Abstract Distributed deep learning workloads include throughput-intensive training tasks on the GPU clusters, where the Distributed Stochastic Gradient Descent (SGD) incurs significant communication delays after backward propagation, forces workers to wait for the gradient synchronization via a centralized parameter server or directly in decentralized workers. We present CrossoverScheduler, an algorithm that enables communication cycles of a distributed training application to be filled by other applications through pipelining communication and computation. With CrossoverScheduler, the running performance of distributed training can be significantly improved without sacrificing convergence rate and network accuracy. We achieve so by introducing Crossover Synchronization which allows multiple distributed deep learning applications to time-share the same GPU alternately. The prototype of CrossoverScheduler is built and integrated with Horovod. Experiments on a variety of distributed tasks show that CrossoverScheduler achieves 20% \\times speedup for image classification tasks on ImageNet dataset. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()