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Massive amounts of data have led to the training of large-scale machinelearning models on a single worker inefficient. Distributed machine learningmethods such as Parallel-SGD have received significant interest as a solutionto tackle this problem. However, the performance of distributed systems doesnot scale linearly with the number of workers due to the high networkcommunication cost for synchronizing gradients and parameters. Researchers haveproposed techniques such as quantization and sparsification to alleviate thisproblem by compressing the gradients. Most of the compression schemes result incompressed gradients that cannot be directly aggregated with efficientprotocols such as all-reduce. In this paper, we present a set of all-reducecompatible gradient compression schemes which significantly reduce thecommunication overhead while maintaining the performance of vanilla SGD. Wepresent the results of our experiments with the CIFAR10 dataset andobservations derived during the process. Our compression methods perform betterthan the in-built methods currently offered by the deep learning frameworks.Code is available at the repository:\url{https://github.com/vineeths96/Gradient-Compression}.

Unbiased Single-scale and Multi-scale Quantizers for Distributed Optimization