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The convergence of SGD based distributed training algorithms is tied to thedata distribution across workers. Standard partitioning techniques try toachieve equal-sized partitions with per-class population distribution inproportion to the total dataset. Partitions having the same overall populationsize or even the same number of samples per class may still have Non-IIDdistribution in the feature space. In heterogeneous computing environments,when devices have different computing capabilities, even-sized partitionsacross devices can lead to the straggler problem in distributed SGD. We developa framework for distributed SGD in heterogeneous environments based on a noveldata partitioning algorithm involving submodular optimization. Our datapartitioning algorithm explicitly accounts for resource heterogeneity acrossworkers while achieving similar class-level feature distribution andmaintaining class balance. Based on this algorithm, we develop a distributedSGD framework that can accelerate existing SOTA distributed training algorithmsby up to 32%.

RCD-SGD: Resource-Constrained Distributed SGD in Heterogeneous Environment via Submodular Partitioning